@article {aCumplido-Mayoral22, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration stratified by sex}, journal = {eLife}, volume = {12}, year = {2023}, month = {04/2023}, abstract = {

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer9s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration.

}, issn = {2050-084X}, doi = {https://doi.org/10.7554/eLife.81067}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Gregory Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Luigi Lorenzini and Carolina Minguillon and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} } @conference {cCasamitjana23, title = {Deep Learning CT segmentation for dosimetry in postoperative endometrial carcinoma treatment}, booktitle = {XLI Congreso Anual de la Sociedad Espa{\~n}ola de Ingenier{\'\i}a Biom{\'e}dica}, year = {2023}, month = {11/2023}, publisher = {Ediciones UPCT. Universidad Polit{\'e}cnica de Cartagena}, organization = {Ediciones UPCT. Universidad Polit{\'e}cnica de Cartagena}, address = {Cartagena}, abstract = {

Prophylactic vaginal brachytherapy (VBT) is a common treatment after tumor resection in endometrial cancer that requires individual delineation of the Clinical Target Volume (CTV). The main aim of this study was to assess the viability of automatic Deep Learning (DL) algorithms for segmenting the CTV from pelvic Computed Tomography images. We collected a dataset of 220 CT images, labeled manually. We implemented and trained V-Net and UNETR networks and we assessed the performance in a cross-validated setting, by dividing them into training, validation, and test sets. We assessed the quality of the segmentations using the Dice Coefficient (DICE) and the 95\% Hausdorff Distance (HD) and using dose-volume histogram parameters. We also evaluated data augmentation (DA) strategies. Both algorithms gave HD values between 8.2-8.7 and DICE of 0.78-0.79. There were no statistical differences in the volume-dose parameters between automatic and manual labels. DA slightly improved the performance of the algorithms. We proved the
applicability of DL for CTV segmentation in postoperative endometrial carcinoma, using two different model networks, UNETR and V-Net, and with DA.

}, url = {https://caseib.es/2023/wp-content/uploads/2023/11/CASEIB-2023-Programa-Final-Res\%C3\%BAmenes.pdf?$\#$page=39}, author = {Adri{\`a} Casamitjana and Roser Sala-Llonch and Raul Tudela and Arnau Andr{\'e}s and Sara Or{\'\i}o and Casas, J. and Cristian Candela-Juan and Antonio Herreros and Aida Ni{\~n}erola-Baiz{\'a}n} } @conference {cHurtado23, title = {Study of Manifold Geometry using Multiscale Non-Negative Kernel Graphs}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, year = {2023}, month = {06/2023}, address = {Rhodes Island, Greece}, isbn = {978-1-7281-6327-7}, doi = {https://doi.org/10.1109/ICASSP49357.2023.10095956}, author = {Hurtado, C. and Shekkizhar, S. and Ruiz-Hidalgo, J. and Ortega, A.} } @conference {cCumplido-Mayoral, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2022}, month = {07/2022}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Greg Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cBonet22, title = {Channel Redundancy and Overlap in Convolutional Neural Networks with Channel-Wise NNK Graphs}, booktitle = {International Conference on Acoustics, Speech and Signal Processing}, year = {2022}, month = {05/2022}, abstract = {

Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret. However, convolutional layers consist of multiple channels that are activated by different types of inputs, which suggests that more insights may be gained by studying the channels and how they relate to each other. In this paper, we first analyze theoretically channel-wise non-negative kernel (CW-NNK) regression graphs, which allow us to quantify the overlap between channels and, indirectly, the intrinsic dimension of the data representation manifold. We find that redundancy between channels is significant and varies with the layer depth and the level of regularization during training. Additionally, we observe that there is a correlation between channel overlap in the last convolutional layer and generalization performance. Our experimental results demonstrate that these techniques can lead to a better understanding of deep representations.\ 

}, author = {Bonet, D. and Ortega, A. and Ruiz-Hidalgo, J. and Shekkizhar, S.} } @conference {cMayoral21a, title = {Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Mahnaz Shekari and Gemma Salvad{\'o} and Greg Operto and Raffaele Cacciaglia and Carles Falcon and Aida Ni{\~n}erola Baiz{\'a}n and Andr{\'e}s Perissinotti and Carolina Minguillon and Karine Fauria and Ivonne Suridjan and Gwendlyn Kollmorgen and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cBonet21, title = {Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation}, booktitle = {Asia Pacific Signal and Information Processing Association Annual Summit, APSIPA}, year = {2021}, month = {12/2021}, address = {Tokyo, Japan}, abstract = {

State-of-the-art neural network architectures continue to scale in size and deliver impressive generalization results, although this comes at the expense of limited interpretability. In particular, a key challenge is to determine when to stop training the model, as this has a significant impact on generalization. Convolutional neural networks (ConvNets) comprise high-dimensional feature spaces formed by the aggregation of multiple channels, where analyzing intermediate data representations and the model{\textquoteright}s evolution can be challenging owing to the curse of dimensionality. We present channel-wise DeepNNK (CW-DeepNNK), a novel channel-wise generalization estimate based on non-negative kernel regression (NNK) graphs with which we perform local polytope interpolation on low-dimensional channels. This method leads to instance-based interpretability of both the learned data representations and the relationship between channels. Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel. Our experiments demonstrate that our proposed method has advantages as compared to the standard criterion based on validation set performance.

}, url = {https://arxiv.org/abs/2107.12972}, author = {Bonet, D. and Ortega, A. and Ruiz-Hidalgo, J. and Shekkizhar, S.} } @unpublished {xBonet21, title = {Improved Neural Network Generalization using Channel-Wise NNK Graph Constructions}, year = {2021}, publisher = {Final Year Project, UPC}, abstract = {

State-of-the-art neural network architectures continue to scale in size and deliver impressive results on unseen data points at the expense of poor interpretability. In the deep layers of these models we often encounter very high dimensional feature spaces, where constructing graphs from intermediate data representations can lead to the well-known curse of dimensionality. We propose a channel-wise graph construction method that works on lower dimensional subspaces and provides a new channel-based perspective that leads to better interpretability of the data and relationship between channels. In addition, we introduce a novel generalization estimate based on the proposed graph construction method with which we perform local polytope interpolation. We show its potential to replace the standard generalization estimate based on validation set performance to perform progressive channel-wise early stopping without requiring a validation set.

}, author = {Bonet, David}, editor = {Ortega, Antonio and Ruiz-Hidalgo, J. and Shekkizhar, Sarath} } @mastersthesis {xOriola, title = {Species-agnostic Local Ancestry Inference on Genomic Data with Convolutions}, year = {2021}, abstract = {

Local Ancestry Inference (LAI) is the high resolution prediction of ancestry (African, European, ...) across a DNA sequence. LAI is becoming increasingly important in DNA sequence analysis for the study of human ancestry and migrations. It is also necessary for polygenic risk scores research (prediction of traits and disease risk). Most current LAI models are built for specific species, set of ancestries and chromosomes, hence a new model needs to be trained from scratch for every slightly different setting. This creates a big barrier for research and industry to shift across different LAI scenarios. In this thesis we present SALAI-Net, the first statistical method for LAI with reference panel that can be used on any set of species and ancestries (species-agnostic). Loter is the state of the art in species-agnostic models with reference panel, and is based on a dynamic programming algorithm. However, it is slow and does not perform very well in small reference panel settings. Our model is based on a novel hand-engineered template matching block followed by a convolutional smoothing filter optimized to minimize cross-entropy loss on a training dataset. The right choice of DNA sequence encoding, similarity features and architecture is what makes our model able to generalize well to unseen ancestries, species, and different chromosomes. We benchmark our models on whole genome data of humans and we test the ability to generalize to dog species when trained on human data. Our models outperform the state-of-the-art method by a big margin in terms of accuracy, testing in different settings and datasets. Moreover, it is up to two orders of magnitude faster. Our model also shows close to no generalization gap when switching between species.

}, author = {Oriol, Benet}, editor = {Mas-Montserrat, Daniel and Ioannidis, Alexander G. and Xavier Gir{\'o}-i-Nieto} } @article {9103248, title = {Grounded Sequence to Sequence Transduction}, journal = {IEEE Journal of Selected Topics in Signal Processing}, volume = {14}, year = {2020}, month = {05/2020}, pages = {577-591}, abstract = {

Speech recognition and machine translation have made major progress over the past decades, providing practical systems to map one language sequence to another. Although multiple modalities such as sound and video are becoming increasingly available, the state-of-the-art systems are inherently unimodal, in the sense that they take a single modality {\textemdash} either speech or text {\textemdash} as input. Evidence from human learning suggests that additional modalities can provide disambiguating signals crucial for many language tasks. In this article, we describe the How2 dataset\ , a large, open-domain collection of videos with transcriptions and their translations. We then show how this single dataset can be used to develop systems for a variety of language tasks and present a number of models meant as starting points. Across tasks, we find that building multimodal architectures that perform better than their unimodal counterpart remains a challenge. This leaves plenty of room for the exploration of more advanced solutions that fully exploit the multimodal nature of the How2 dataset\ , and the general direction of multimodal learning with other datasets as well.

}, author = {L. Specia and L. Barrault and O. Caglayan and Amanda Duarte and D. Elliott and S. Gella and N. Holzenberger and C. Lala and S. J. Lee and J. Libovicky and P. Madhyastha and F. Metze and K. Mulligan and A. Ostapenko and S. Palaskar and R. Sanabria and J. Wang and R. Arora} } @article {aCasamitjanab, title = {NeAT: a nonlinear analysis toolbox for neuroimaging}, journal = {Neuroinformatics}, year = {2020}, month = {03/2020}, abstract = {

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer{\textquoteright}s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.

}, keywords = {Alzheimer{\textquoteright}s disease, APOE, GAM, GLM, inference, neuroimaging, nonlinear., SVR}, doi = {10.1007/s12021-020-09456-w}, url = {https://link.springer.com/article/10.1007/s12021-020-09456-w}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Santi Puch and Asier Aduriz and Carlos Lopez and G. Operto and R. Cacciaglia and C. Falcon and J.L. Molinuevo and Juan D. Gispert} } @conference {cBernal, title = {Transcription-Enriched Joint Embeddings or Spoken Descriptions of Images and Videos}, booktitle = {CVPR 2020 Workshop on Egocentric Perception, Interaction and Computing}, year = {2020}, month = {06/2020}, publisher = {arXiv}, organization = {arXiv}, address = {Seattle, WA, USA}, abstract = {

In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives. The proposed methodology departs from a baseline system that spawns a embedding space trained with only spoken narratives and image cues. Our experiments on the EPIC-Kitchen and Places Audio Caption datasets show that introducing the human-generated textual transcriptions of the spoken narratives helps to the training procedure yielding to get better embedding representations. The triad speech, image and words allows for a better estimate of the point embedding and show an improving of the performance within tasks like image and speech retrieval,\ even when text third modality, text, is not present in the task.

Transcription-Enriched Joint Embeddings for Spoken Descriptions of Images and Videos from Universitat Polit{\`e}cnica de Catalunya
}, author = {Oriol, Benet and Luque, J. and Diego, Ferran and Xavier Gir{\'o}-i-Nieto} } @conference {cGorrizc, title = {Assessing Knee OA Severity with CNN attention-based end-to-end architectures}, booktitle = {International Conference on Medical Imaging with Deep Learning (MIDL) 2019}, year = {2019}, month = {02/2019}, publisher = {JMLR}, organization = {JMLR}, address = {London, United Kingdom}, abstract = {

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to\ automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which\ incorporates trainable attention modules acting as unsupervised fine-grained detectors of\ the region of interest (ROI). The proposed attention modules can be applied at different\ levels and scales across any CNN pipeline helping the network to learn relevant attention\ patterns over the most informative parts of the image at different resolutions. We test\ the proposed attention mechanism on existing state-of-the-art CNN architectures as our\ base models, achieving promising results on the benchmark knee OA datasets from the\ osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All the codes\ from our experiments will be publicly available on the github repository: https://github.com/marc-gorriz/KneeOA-CNNAttention

}, url = {http://proceedings.mlr.press/v102/gorriz19a.html}, author = {G{\'o}rriz, Marc and Antony, Joseph and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cCasamitjana19, title = {Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2019}, month = {07/2019}, author = {Adri{\`a} Casamitjana and P. Petrone and C. Falcon and M. Artigues and G. Operto and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @article {aCasamitjana19, title = {Detection of Amyloid-Positive Cognitively Unimpaired Individuals Using Voxel-Based Machine Learning on Structural Longitudinal Brain MRI}, journal = {Alzheimer{\textquoteright}s \& Dementia}, volume = {15}, year = {2019}, month = {07/2019}, chapter = {752}, abstract = {

Magnetic resonance imaging (MRI) has unveiled specific AD alterations at different stages of the AD pathophysiologic continuum that constitutes what has been established as the {\textquoteleft}AD signature{\textquoteright}. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in unimpaired individuals is still an area open for exploration.

}, issn = {1552-5260}, doi = {10.1016/j.jalz.2019.06.2796}, author = {Adri{\`a} Casamitjana and P. Petrone and C. Falcon and M. Artigues and G. Operto and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @article {aRoisman18, title = {Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas}, journal = {British Journal of Haematology}, volume = {184}, year = {2019}, month = {Feb 2019}, pages = {373-383}, issn = {ISSN:1365-2141}, doi = {DOI: 10.1111/bjh.15656}, author = {A. Roisman and A. Navarro and G. Clot and G. Castellano and B. Gonzalez-Farre and P. P{\'e}rez-Galan and A. Esteve and M. Dabad and S. Heath and M. Gut and Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras and I. Slavusky and L. Magnano and H. Horn and A. Rosenwald and G. Ott and M. Aymerich and A. L{\'o}pez-Guillermo and P. Jares and J.I. Mart{\'\i}n-Subero and E. Campo and L. Hern{\'a}ndez} } @conference {cOriol, title = {Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation}, booktitle = {NeurIPS 2019 Workshop on AI for Social Good}, year = {2019}, month = {09/2019}, address = {Vancouver, Canada}, abstract = {

This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge.\ Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning.\ However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact.\ Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture.\ While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes.\ In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused.

Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation from Universitat Polit{\`e}cnica de Catalunya

\>
}, author = {Oriol, Benet and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xOriol, title = {Multimodal Hate Speech Detection in Memes}, year = {2019}, abstract = {

This thesis explores a multimodal approach to Hate Speech detection, involving vision and language (text). More specifically, we deal with the context of memes, a form of internet humour which will present additional challenges.\ We first gather meme data from different sources. This way, we create a hate memes dataset for this task. Then, we use this data for the training and evaluation of statistical models, which are based on state-of-the art neural networks.\ We study different ways to fine-tune pretrained descriptors for our specific task. We propose a way to add expert knowledge into the system and orient it into a real world issue-solving system. We also discuss ways to deal with the issue of reduced amount of data, experimenting with a self-supervised learning approach for pre-training.\ We also compare the effect or contribution of each modality in the overall performance of the model.

}, author = {Oriol, Benet}, editor = {Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @article {aPetrone, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI}, journal = {Alzheimer{\textquoteright}s Research \& Therapy}, volume = {11}, year = {2019}, month = {12/2019}, abstract = {

Background: Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer{\textquoteright}s disease (AD) pathophysiologic continuum constituting what has been established as {\textquoteleft}AD signature{\textquoteright}. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration.

Method: Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cut-offs (\<192pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting.

Results: The optimal follow-up time for classification of Ctrls vs PreAD was Δt\>2.5 years and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC=0.87 (95\%CI:0.72-0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior and lateral temporal lobes, precuneus, caudate heads, basal forebrain and lateral ventricles.

Conclusions: Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid positive individuals, this longitudinal voxelwise classifier is expected to avoid 55\% of unnecessary CSF and/or PET scans and reduce economic cost by 40\%.

}, doi = {https://doi.org/10.1186/s13195-019-0526-8}, url = {https://link.springer.com/article/10.1186/s13195-019-0526-8}, author = {Paula Petrone and Adri{\`a} Casamitjana and Carles Falcon and Miguel Artigues C{\`a}naves and G. Operto and R. Cacciaglia and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cLinardosa, title = {Simple vs complex temporal recurrences for video saliency prediction}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2019}, month = {09/2019}, publisher = {British Machine Vision Association}, organization = {British Machine Vision Association}, address = {Cardiff, Wales / UK.}, abstract = {

This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal domain. The first modification is the addition of a ConvLSTM within the architecture, while the second is a computationally simple exponential moving average of an internal convolutional state. We use weights pre-trained on the SALICON dataset and fine-tune our model on DHF1K. Our results show that both modifications achieve state-of-the-art results and produce similar saliency maps.

}, url = {https://bmvc2019.org/wp-content/uploads/papers/0952-paper.pdf}, author = {Linardos, Panagiotis and Mohedano, Eva and Nieto, Juan Jos{\'e} and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto and McGuinness, Kevin} } @article {aKuijf, title = {Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {03/2019}, abstract = {

Quantification of white matter hyperintensities (WMH) of presumed vascular origin is of key\ importance in many neurological research studies. Advanced\ measurements are obtained from manual segmentations on brain\ MR images, which is a laborious procedure. Automatic WMH\ segmentation methods exist, but a standardized comparison of\ such methods is lacking. We organized a scientific challenge, in\ which developers could evaluate their method on a standardized\ multi-center/-scanner image dataset, giving an objective comparison:\ the WMH Segmentation Challenge (http://wmh.isi.uu.nl/).\ Sixty T1+FLAIR images from three MR scanners were released\ with manual WMH segmentations. A secret test set of 110\ images from five MR scanners was used for evaluation. Methods\ had to be containerized and submitted to the challenge organizers.Five evaluation metrics were used to rank the methods:\ (1) Dice Similarity Coefficient, (2) modified Hausdorff distance\ (95th percentile), (3) absolute percentage volume difference, (4)\ sensitivity for detecting individual lesions, and (5) F1-score for\ individual lesions. Additionally, methods were ranked on their\ inter-scanner robustness.\ Twenty participants submitted their method for evaluation.\ This paper provides a detailed analysis of the results. In brief,there is a cluster of four methods that rank significantly better\ than the other methods. There is one clear winner, which also\ has the best inter-scanner robustness.\ The challenge remains open for future submissions and provides\ a public platform for method evaluation.

}, keywords = {brain, Evaluation and performance, Magnetic resonance imaging (MRI), segmentation}, issn = {0278-0062}, doi = {10.1109/TMI.2019.2905770}, author = {Hugo Kuijf and Matthijs Biesbroek and Jeroen de Bresser and Rutger Heinen and Simon Andermatt and Mariana Bento and Matt Berseth and Mikhail Belyaev and Jorge Cardoso and Adri{\`a} Casamitjana and Louis Collins and Mahsa Dadar and Achileas Georgiou and Mohsen Ghafoorian and Dakai Jin and April Khademi and Jesse Knight and Hongwei Li and Xavier Llado and Miguel Luna and Qaiser Mahmood and Richard McKinley and Alireza Mehrtash and Sebastien Ourselin and Bo-yong Park and Hyunkin Park and Sang Hyun Park and Simon Pezold and Elodie Puybareau and Leticia Rittner and Carole Sudre and Sergi Valverde and Ver{\'o}nica Vilaplana and Rolan Wiest and Yongchao Xu and Ziyue Xu and Guodong Zeng and Jianguo Zhang and Guoyan Zheng and Christoper Chen and Wiesje van der Flier and Frederik Barkhof and Max Viergever and Geert Jan Biessels} } @article {aPetrone18, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature.}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {14}, year = {2018}, month = {07/2018}, pages = {P1235}, doi = {10.1016/j.jalz.2018.06.1737}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @conference {cPetrone, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2018}, month = {07/2018}, address = {Chicago, USA}, abstract = {

In the last decade, magnetic resonance imaging has unveiled specific AD alterations at different stages of the AD pathophysiologic continuum that conform what has been established as the AD signature. To which extent MRI can detect structural changes at the preclinical asymptomatic stage of AD - the preclinical AD signature- is still an area open for exploration. Our work supports the idea that there are brain volumetric changes specific to preclinical AD subjects and defines the preclinical AD signature based on longitudinal data. While some regions show a pattern of atrophy that overlaps with the AD signature, other specific regions exhibit changes that are unique to this early asymptomatic AD stage.

}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @conference {cGomez, title = {Demonstration of an Open Source Framework for Qualitative Evaluation of CBIR Systems}, booktitle = {ACM Multimedia}, year = {2018}, month = {10/2018}, publisher = {ACM}, organization = {ACM}, address = {Seoul, South Korea}, abstract = {

Evaluating image retrieval systems in a quantitative way, for example by computing measures like mean average precision, allows for objective comparisons with a ground-truth. However, in cases where ground-truth is not\  available, the only alternative is to collect feedback from a user. Thus, qualitative assessments become important to better understand how the system works. Visualizing the results could be, in some scenarios, the only way to evaluate the results obtained and also the only opportunity to identify that a system is failing. This necessitates developing a User Interface (UI) for a Content Based Image Retrieval (CBIR) system that allows visualization of results and improvement via capturing user relevance feedback. A well-designed UI facilitates understanding of the performance of the system, both in cases where it works well and perhaps more importantly those which highlight the need for improvement. Our open-source system implements three components to facilitate researchers to quickly develop these capabilities for their retrieval engine. We present: a web-based user interface to visualize retrieval results and collect user annotations; a server that simplifies\  connection with any underlying CBIR system; and a server that manages the search engine data.\ 

User Interface for an Image Retrieval Engine System from Universitat Polit{\`e}cnica de Catalunya
}, doi = {10.1145/3240508.3241395}, url = {https://dl.acm.org/citation.cfm?id=3241395}, author = {Gomez, Paula and Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cAssensa, title = {PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC)}, year = {2018}, month = {07/2018}, publisher = {Springer}, organization = {Springer}, address = {Munich, Germany}, abstract = {

We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples. A visual scanpath is defined as the sequence of fixation points over an image defined by a human observer with its gaze. PathGAN is composed of two parts, the generator and the discriminator. Both parts extract features from images using off-the-shelf networks, and train recurrent layers to generate or discriminate scanpaths accordingly. In scanpath prediction, the stochastic nature of the data makes it very difficult to generate realistic predictions using supervised learning strategies, but we adopt adversarial training as a suitable alternative. Our experiments prove how PathGAN improves the state of the art of visual scanpath prediction on the Salient360! dataset.

This work obtained the\ 2nd award in Prediction of Head-gaze Scan-paths for Images, and the 2nd award in Prediction of Eye-gaze Scan-paths for Images at the IEEE ICME 2018 Salient360! Challenge.

}, doi = {10.1007/978-3-030-11021-5_25}, url = {https://doi.org/10.1007/978-3-030-11021-5_25}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cMohedanob, title = {Saliency Weighted Convolutional Features for Instance Search}, booktitle = {Content-Based Multimedia Indexing - CBMI}, year = {2018}, month = {09/2018}, publisher = {IEEE}, organization = {IEEE}, address = {La Rochelle, France}, abstract = {

This work explores attention models to weight the contribution of local convolutional representations for the instance search task. We present a retrieval framework based on bags of local convolutional features (BLCF) that benefits from saliency weighting to build an efficient image representation. The use of human visual attention models (saliency) allows significant improvements in retrieval performance without the need to conduct region analysis or spatial verification, and without requiring any feature fine tuning. We investigate the impact of different saliency models, finding that higher performance on saliency benchmarks does not necessarily equate to improved performance when used in instance search tasks. The proposed approach outperforms the state-of-the-art on the challenging INSTRE benchmark by a large margin, and provides similar performance on the Oxford and Paris benchmarks compared to more complex methods that use off-the-shelf representations.

Saliency Weighted Convolutional Features for Instance Search from Universitat Polit{\`e}cnica de Catalunya
}, author = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @article {aAssens, title = {Scanpath and Saliency Prediction on 360 Degree Images}, journal = {Elsevier Signal Processing: Image Communication}, year = {2018}, abstract = {

We introduce deep neural networks for scanpath and saliency prediction trained on 360-degree images. The scanpath prediction model called SaltiNet is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation using a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. We also show how a similar architecture achieves state-of-the-art performance for the related task of saliency map prediction. Our source code and trained models available here.

}, url = {https://www.sciencedirect.com/science/article/pii/S0923596518306209}, author = {Assens, Marc and McGuinness, Kevin and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @inbook {bBellot17, title = {Efficient Combination of Pairwise Feature Networks}, booktitle = {Neural Connectomics Challenge}, year = {2017}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {7}, issn = {978-3-319-53069-7}, doi = {10.1007/978-3-319-53070-3}, author = {P Bellot and Patrick Meyer}, editor = {Battaglia, D and Guyon, I. and Lemaire, V. and Orlandi, J. and Ray, B. and Soriano, J.} } @unpublished {xMohedanoa, title = {Fine-tuning of CNN models for Instance Search with Pseudo-Relevance Feedback}, year = {2017}, publisher = {NIPS 2017 Women in Machine Learning Workshop}, address = {Long Beach, CA, USA}, abstract = {

CNN classification models trained on millions of labeled images have been proven to encode {\textquotedblleft}general purpose{\textquotedblright} descriptors in their intermediate layers. These descriptors are useful for a diverse range of computer vision problems~\cite{1}. However, the target task of these models is substantially different to the instance search task. While classification is concerned with distinguishing between different classes, instance search is concerned with identifying concrete instances of a particular class.\ 

In this work we propose an unsupervised approach to finetune a model for similarity learning~\cite{2}. For that, we combine two different search engines: one based on off-the-shelf CNN features, and another one on the popular SIFT features. As shown in the figure below, we observe that the information of pre-trained CNN representations and SIFT is in most of the cases complementary, which allows the generation of high quality rank lists. The fusion of the two rankings is used to generate training data for a particular dataset. A pseudo-relevance feedback strategy~\cite{3} is used for sampling images from rankings, considering the top images as positive examples of a particular instance and middle-low ranked images as negative examples.

}, author = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cSalvadorc, title = {Learning Cross-modal Embeddings for Cooking Recipes and Food Images}, booktitle = {CVPR}, year = {2017}, month = {03/2017}, publisher = {CVF / IEEE}, organization = {CVF / IEEE}, address = {Honolulu, Hawaii, USA}, abstract = {

In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images. As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high-capacity models on aligned, multi-modal data. Using these data, we train a neural network to find a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Additionally, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M dataset and food and cooking in general.

[Project page]

In the news:

}, doi = {10.1109/CVPR.2017.327}, url = {http://openaccess.thecvf.com/content_cvpr_2017/html/Salvador_Learning_Cross-Modal_Embeddings_CVPR_2017_paper.html}, author = {Amaia Salvador and Hynes, Nicholas and Aytar, Yusuf and Marin, Javier and Ofli, Ferda and Weber, Ingmar and Torralba, Antonio} } @conference {cPetrone17, title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening}, booktitle = {Alzheimer{\textquoteright}s Association International Conference, London, 2017}, year = {2017}, month = {07/2017}, author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and A. Tucholka and C. Falcon and R. Cacciaglia and G. Operto and S. Skouras and J.L. Molinuevo and J.D. Gispert} } @article {aPetrone17, title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {13}, year = {2017}, month = {07/2017}, pages = {P1245}, doi = {10.1016/j.jalz.2017.07.457}, url = {https://doi.org/10.1016/j.jalz.2017.07.457}, author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and D. Sanchez-Escobedo and A. Tucholka and R. Cacciaglia and G. Operto and S. Skouras and C. Falcon and J.L. Molinuevo and J.D. Gispert} } @inbook {bMohedano17, title = {Object Retrieval with Deep Convolutional Features}, booktitle = {Deep Learning for Image Processing Applications}, volume = {31}, number = {Advances in Parallel Computing}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam, The Netherlands}, abstract = {

Image representations extracted from convolutional neural networks (CNNs) outdo hand-crafted features in several computer vision tasks, such as visual image retrieval. This chapter recommends a simple pipeline for encoding the local activations of a convolutional layer of a pretrained CNN utilizing the well-known Bag of Words (BoW) aggregation scheme and called bag of local convolutional features (BLCF). Matching each local array of activations in a convolutional layer to a visual word results in an assignment map, which is a compact representation relating regions of an image with a visual word. We use the assignment map for fast spatial reranking, finding object localizations that are used for query expansion. We show the suitability of the BoW representation based on local CNN features for image retrieval, attaining state-of-the-art performance on the Oxford and Paris buildings benchmarks. We demonstrate that the BLCF system outperforms the latest procedures using sum pooling for a subgroup of the challenging TRECVid INS benchmark according to the mean Average Precision (mAP) metric.

}, issn = {978-1-61499-822-8 }, doi = {10.3233/978-1-61499-822-8-137}, url = {http://ebooks.iospress.nl/volumearticle/48028}, author = {Mohedano, Eva and Amaia Salvador and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Marqu{\'e}s, F.} } @conference {cPana, title = {SalGAN: Visual Saliency Prediction with Generative Adversarial Networks}, booktitle = {CVPR 2017 Scene Understanding Workshop (SUNw)}, year = {2017}, address = {Honolulu, Hawaii, USA}, abstract = {

We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples. The first stage of the network consists of a generator model whose weights are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency maps. The resulting prediction is processed by a discriminator network trained to solve a binary classification task between the saliency maps generated by the generative stage and the ground truth ones. Our experiments show how adversarial training allows reaching state-of-the-art performance across different metrics when combined with a widely-used loss function like BCE.

}, url = {https://arxiv.org/abs/1701.01081}, author = {Pan, Junting and Cristian Canton-Ferrer and McGuinness, Kevin and O{\textquoteright}Connor, N. and Jordi Torres and Elisa Sayrol and Xavier Gir{\'o}-i-Nieto} } @conference {cAssens, title = {SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes}, booktitle = {ICCV Workshop on Egocentric Perception, Interaction and Computing}, year = {2017}, month = {07/2017}, publisher = {IEEE}, organization = {IEEE}, address = {Venice, Italy}, abstract = {

We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks.

Winner of three awards at the Salient 360 Challenge at IEEE ICME 2017 (Hong Kong): Best Scan Path, Best Student Scan-path and Audience Award.

SaltiNet: The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, isbn = {978-1-5386-1034-3}, doi = {10.1109/ICCVW.2017.275}, url = {http://ieeexplore.ieee.org/document/8265485/}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @mastersthesis {xAssens, title = {The Temporal Dimension of Visual Attention Models}, year = {2017}, abstract = {

Program: Bachelor Degree on Telecommunications Science and Technologies (CITTEL)

Grade: A with honours (10.0/10.0)

This thesis explores methodologies for scanpath prediction on images using deep learning\ frameworks.\ As a preliminary step, we analyze the characteristics of the data provided by di erent datasets.\ We then explore the use of Convolutional Neural Networks (CNN) and Long-Short-Term-Memory\ (LSTM) newtworks for scanpath prediction. We observe that these models fail due to the high\ stochastic nature of the data.\ With the gained insight, we propose a novel time-aware visual saliency representation named\ Saliency Volume, that averages scanpaths over multiple observers.\ Next, we explore the SalNet network and adapt it for saliency volume prediction, and we find\ several ways of generating scanpaths from saliency volumes.\ Finally, we ne-tuned our model for scanpaht prediction on 360-degree images and successfully\ submitted it to the Salient360! Challenge from ICME. The source code and models are publicly\ available at https://github.com/massens/saliency-360salient-2017.

The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, author = {Assens, Marc}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and Noel E. O{\textquoteright}Connor} } @conference {cLe, title = {Towards large scale multimedia indexing: A case study on person discovery in broadcast news}, booktitle = {International Workshop on Content-Based Multimedia Indexing - CBMI 2017}, year = {2017}, month = {06/2017}, address = {Firenze, Italy}, abstract = {

The rapid growth of multimedia databases and the human interest in their peers make indices representing the location and identity of people in audio-visual documents essential for searching archives. Person discovery in the absence of prior identity knowledge requires accurate association of audio-visual cues and detected names. To this end, we present 3 different strategies to approach this problem: clustering-based naming, verification-based naming, and graph-based naming. Each of these strategies utilizes different recent advances in unsupervised face / speech representation, verification, and optimization. To have a better understanding of the approaches, this paper also provides a quantitative and qualitative comparative study of these approaches using the associated corpus of the Person Discovery challenge at MediaEval 2016. From the results of our experiments, we can observe the pros and cons of each approach, thus paving the way for future promising research directions.

}, doi = {https://doi.org/10.1145/3095713.3095732}, author = {Nam Le and Herv{\'e} Bredin and Gabriel Sargent and Miquel India and Paula Lopez-Otero and Claude Barras and Camille Guinaudeau and Guillaume Gravier and Gabriel Barbosa da Fonseca and Izabela Lyon Freire and Zenilton Patroc{\'\i}nio Jr. and Silvio Jamil F. Guimaraes and Gerard Mart{\'\i} and Morros, J.R. and Javier Hernando and Laura Docio-Fernandez and Carmen Garcia-Mateo and Sylvain Meignier and Jean-Marc Odobez} } @conference {cMohedanoa, title = {Bags of Local Convolutional Features for Scalable Instance Search}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)}, year = {2016}, month = {06/2016}, publisher = {ACM}, organization = {ACM}, address = {New York City, NY; USA}, abstract = {

Image representations extracted from convolutional neural networks (CNNs) have been shown to outperform hand-crafted features in multiple computer vision tasks, such as visual image retrieval. This work proposes a simple pipeline for encoding the local activations of a convolutional layer of a pre-trained CNN using the well-known bag of words aggregation scheme (BoW). Assigning each local array of activations in a convolutional layer to a visual word produces an \textit{assignment map}, a compact representation that relates regions of an image with a visual word. We use the assignment map for fast spatial reranking, obtaining object localizations that are used for query expansion. We demonstrate the suitability of the Bag of Words representation based on local CNN features for image retrieval, achieving state-of-the-art performance on the Oxford and Paris buildings benchmarks. We show that our proposed system for CNN feature aggregation with BoW outperforms state-of-the-art techniques using sum pooling at a subset of the challenging TRECVid INS benchmark.

Best poster award at ACM ICMR 2016

Preprint on arXiv

Project page with source code

Post on GitXiv

Overall acceptance rate in ICMR 2016: 30\%\ 

2016-05-Seminar-AmaiaSalvador-DeepVision from Image Processing Group on Vimeo.

Convolutional Features for Instance Search from Xavier Giro
}, keywords = {Bag of Words, Convolutional Neural Networks, Instance Retrieval}, isbn = {978-1-4503-4359-6}, doi = {http://dx.doi.org/10.1145/2911996.2912061}, url = {http://dx.doi.org/10.1145/2911996.2912061}, author = {Mohedano, Eva and Amaia Salvador and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Marqu{\'e}s, F.} } @conference {cMarsden, title = {Dublin City University and Partners{\textquoteright} Participation in the INS and VTT Tracks at TRECVid 2016}, booktitle = {TRECVID Workshop 2016}, year = {2016}, month = {11/2016}, address = {Gaithersburg, MD, USA}, abstract = {

DCU participated with a consortium of colleagues from NUIG and UPC in two tasks,\ INS and VTT. For the INS task we developed a framework consisting of face detection and\ representation and place detection and representation, with a user annotation of top-ranked\ videos. For the VTT task we ran 1,000 concept detectors from the VGG-16 deep CNN on\ 10 keyframes per video and submitted 4 runs for caption re-ranking, based on BM25, Fusion,\ Word2Vec and a fusion of baseline BM25 and Word2Vec. With the same pre-processing for\ caption generation we used an open source image-to-caption CNN-RNN toolkit NeuralTalk2\ to generate a caption for each keyframe and combine them.

}, url = {http://doras.dcu.ie/21484/}, author = {Marsden, Mark and Mohedano, Eva and McGuinness, Kevin and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Zhou, Jiang and Azevedo, Lucas and Daubert, Tobias and Davis, Brian and H{\"u}rlimann, Manuela and Afli, Haithem and Du, Jinhua and Ganguly, Debasis and Li, Wei and Way, Andy and Smeaton, Alan F.} } @conference {cPan, title = {Shallow and Deep Convolutional Networks for Saliency Prediction}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition, CVPR}, year = {2016}, month = {06/2016}, publisher = {Computer Vision Foundation / IEEE}, organization = {Computer Vision Foundation / IEEE}, address = {Las Vegas, NV, USA}, abstract = {

The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train end-to-end architectures that are both fast and accurate. Two designs are proposed: a shallow convnet trained from scratch, and a another deeper solution whose first three layers are adapted from another network trained for classification. To the authors knowledge, these are the first end-to-end CNNs trained and tested for the purpose of saliency prediction.

}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pan_Shallow_and_Deep_CVPR_2016_paper.pdf}, author = {Pan, Junting and McGuinness, Kevin and Elisa Sayrol and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @article {xReyes, title = {Where did I leave my phone ?}, year = {2016}, month = {06/2016}, institution = {4th Workshop on Egocentric (First-Person) Vision, CVPR 2016}, type = {Extended abstract}, address = {Las Vegas, NV, USA}, abstract = {

The interest of users in having their lives digitally recorded has grown in the last years thanks to the advances on wearable sensors.\ Wearable cameras are one of the most informative ones, but they generate large amounts of images that require automatic analysis to build useful applications upon them.\ In this work we explore the potential of these devices to find the last appearance of personal objects among the more than 2,000 images that are generated everyday.\ This application could help into developing personal assistants capable of helping users when they do not remember where they left their personal objects.\ We adapt a previous work on instance search to the specific domain of egocentric vision.

Extended abstract presented as poster in the 4th Workshop on Egocentric (First-Person) Vision,\ CVPR 2016.

}, author = {Reyes, Cristian and Mohedano, Eva and McGuinness, Kevin and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @conference {cReyes, title = {Where is my Phone? Personal Object Retrieval from Egocentric Images}, booktitle = {Lifelogging Tools and Applications Workshop in ACM Multimedia}, year = {2016}, month = {10/2016}, publisher = {ACM}, organization = {ACM}, address = {Amsterdam, The Netherlands}, abstract = {

This work presents a retrieval pipeline and evaluation scheme for the problem of finding the last appearance of personal objects in a large dataset of images captured from a wearable camera. Each personal object is modelled by a small set of images that define a query for a visual search engine.The retrieved results are reranked considering the temporal timestamps of the images to increase the relevance of the later detections. Finally, a temporal interleaving of the results is introduced for robustness against false detections. The Mean Reciprocal Rank is proposed as a metric to evaluate this problem. This application could help into developing personal assistants capable of helping users when they do not remember where they left their personal belongings.

}, doi = {http://dx.doi.org/10.1145/2983576.2983582}, url = {http://arxiv.org/abs/1608.08139}, author = {Reyes, Cristian and Mohedano, Eva and McGuinness, Kevin and Noel E. O{\textquoteright}Connor and Xavier Gir{\'o}-i-Nieto} } @conference {cMohedano, title = {Exploring EEG for Object Detection and Retrieval}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR) }, year = {2015}, address = {Shanghai, China}, abstract = {

This paper explores the potential for using Brain Computer Interfaces (BCI) as a relevance feedback mechanism in content-based image retrieval. We investigate if it is possible to capture useful EEG signals to detect if relevant objects are present in a dataset of realistic and complex images. \ We perform several experiments using a rapid serial visual presentation (RSVP) of images at different rates (5Hz and 10Hz) on 8 users with different degrees of familiarization with BCI and the dataset. We then use the feedback from the BCI and mouse-based interfaces to retrieve objects in a subset of TRECVid images. We show that it is indeed possible detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others. When the users have limited time to annotate the images (100 seconds in our experiments) both interfaces are comparable in performance. Comparing our best users in a retrieval task, we found that EEG-based relevance feedback outperforms mouse-based feedback. The realistic and complex image dataset differentiates our work from previous studies on EEG for image retrieval.

[Extended version in arXiv:1504.02356]

[ACM ICMR 2015 website]

Overall acceptance rate: 33\% (source)

}, doi = {10.1145/2671188.2749368}, url = {http://arxiv.org/abs/1504.02356}, author = {Mohedano, Eva and Amaia Salvador and Porta, Sergi and Xavier Gir{\'o}-i-Nieto and Kevin McGuinness and Healy, Graham and O{\textquoteright}Connor, N.} } @article {aMohedano, title = {Improving Object Segmentation by using EEG signals and Rapid Serial Visual Presentation}, journal = {Multimedia Tools and Applications}, year = {2015}, month = {07/2015}, abstract = {

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When a block of pixels is displayed, we estimate the probability of that block containing the object of interest using a score based on EEG activity. After several such blocks are displayed in rapid visual serial presentation, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study extends our previous work that showed how BCI and simple EEG analysis are useful in locating object boundaries in images

}, issn = {1573-7721}, doi = {10.1007/s11042-015-2805-0}, url = {http://dx.doi.org/10.1007/s11042-015-2805-0}, author = {Mohedano, Eva and Healy, Graham and Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cVentura, title = {Improving Spatial Codification in Semantic Segmentation}, booktitle = {IEEE International Conference on Image Processing (ICIP), 2015}, year = {2015}, month = {09/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Quebec City}, abstract = {

This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition the image into three regions for each object to be described: Figure, Border and Ground. This partition aims at minimizing the influence of the image context on the object description and vice versa by introducing an intermediate zone around the object contour. Furthermore, we also propose a richer visual descriptor of the object by applying a Spatial Pyramid over the Figure region. Two novel Spatial Pyramid configurations are explored: Cartesian-based and crown-based Spatial Pyramids. We test these approaches with state-of-the-art techniques and show that they improve the Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic segmentation challenges.

Improving Spatial Codification in Semantic Segmentation from Xavier Giro
}, url = {http://arxiv.org/abs/1505.07409}, author = {Ventura, C. and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Kevin McGuinness and Marqu{\'e}s, F. and O{\textquoteright}Connor, N.} } @article {xVentura, title = {Improving Spatial Codification in Semantic Segmentation (Supplementary Material)}, year = {2015}, month = {09/2015}, abstract = {

This document contains supplementary material for the paper "Improving Spatial Codification in Semantic Segmentation" submitted to ICIP 2015. First, there is a section dedicated to the results obtained by categories when ideal object candidates (ground truth masks) are used. Then, an analysis of the results using CPMC and MCG object candidates also detailed by categories. Finally, visual results for CPMC and MCG are showed.

}, author = {Ventura, C. and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Kevin McGuinness and Marqu{\'e}s, F. and Noel E. O{\textquoteright}Connor} } @conference {cMcGuinnessa, title = {Insight DCU at TRECVID 2015}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

Insight-DCU participated in the instance search (INS), semantic indexing (SIN), and localization tasks (LOC) this year.

In the INS task we used deep convolutional network features trained on external data and the query data for this year to train our system. We submitted four runs, three based on convolutional network features, and one based on SIFT/BoW. F A insightdcu 1 was an automatic run using features from the last convolutional layer of a deep network with bag-of-words encoding and achieved 0.123 mAP. F A insightdcu 2 modi ed the previous run to use re-ranking based on an R-CNN model and achieved 0.111 mAP. I A insightdcu 3, our interactive run, achieved 0.269 mAP. Our SIFT-based run F A insightdcu 2 used weak geometric consistency to improve performance over the previous year to 0.187 mAP. Overall we found that using features from the convolutional layers improved performance over features from the fully connected layers used in previous years, and that weak geometric consistency improves performance for local feature ranking.

In the SIN task we again used convolutional network features, this time netuning a network pretrained on external data for the task. We submitted four runs, 2C D A insightdcu.15 1..4 varying the top-level learning algorithm and use of concept co-occurance. 2C D A insightdcu.15 1 used a linear SVM top-level learner, and achieved 0.63 mAP. Exploiting concept co-occurance improved the accuracy of our logistic regression run 2C D A insightdcu.15 3 from 0.058 mAP to 0.6 2C D A insightdcu.15 3.

Our LOC system used training data from IACC.1.B and features similar to our INS run, but using a VLAD encoding instead of a bag-of-words. Unfortunately there was problem with the run that we are still investigating.

Note: UPC and NII participated only in the INS task of this submission.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Kevin McGuinness and Mohedano, Eva and Amaia Salvador and Zhang, ZhenXing and Marsden, Mark and Wang, Peng and Jargalsaikhan, Iveel and Antony, Joseph and Xavier Gir{\'o}-i-Nieto and Satoh, Shin{\textquoteright}ichi and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @article {aBellot Pujalte15, title = {NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference}, journal = {BMC Bioinformatics}, volume = {16}, year = {2015}, month = {09/2015}, abstract = {

Background:
In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.

Results:
Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.

Conclusions:
The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

}, keywords = {Benchmark, Bioconductor package, Gene expression, Gene regulation network reconstruction, Gene regulatory networks, Synthetic genetic networks}, doi = {10.1186/s12859-015-0728-4}, url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0728-4}, author = {P. Bellot and Olsen, Catharina and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @mastersthesis {xPorta, title = {Rapid Serial Visual Presentation for Relevance Feedback in Image Retrieval with EEG Signals}, year = {2015}, abstract = {

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Grade: A (9/10)

This thesis explores the potential of relevance feedback for image retrieval using EEG signals for human-computer interaction. This project aims at studying the optimal parameters of a rapid serial visual presentation (RSVP) of frames from a video database when the user is searching for an object instance. The simulations reported in this thesis assess the trade-off between using a small or a large amount of images in each RSVP round that captures the user feedback. While short RSVP rounds allow a quick learning of the user intention from the system, RSVP rounds must also be long enough to let users generate the P300 EEG signals which are triggered by relevant images. This work also addresses the problem of how to distribute potential relevant and non-relevant images in a RSVP round to maximize the probabilities of displaying each relevant frame separated at least 1 second from another relevant frame, as this configuration generates a cleaner P300 EEG signal. The presented simulations are based on a realistic set up for video retrieval with a subset of 1,000 frames from the TRECVID 2014 Instance Search task.

}, keywords = {eeg, feedback, image, relevance, retrieval}, author = {Porta, Sergi}, editor = {Amaia Salvador and Mohedano, Eva and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {Bell1512:Study, title = {Study of Normalization and Aggregation Approaches for Consensus Network Estimation}, booktitle = {2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Artificial Life (2015 IEEE ALIFE)}, year = {2015}, address = {Cape Town, South Africa}, abstract = {

Inferring gene regulatory networks from expression data is a very difficult problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that the different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this paper, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because the are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.

}, author = {P. Bellot and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @phdthesis {dBosio14, title = {Hierarchical information representation and efficient classification of gene expression microarray data}, volume = {PhD Thesis}, year = {2014}, month = {06/2014}, pages = {150}, school = {Universitat Politecnica de Catalunya}, address = {Barcelona}, abstract = {

In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and efficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample classification has not been found yet.

Microarray classification is a complicated task, not only due to the high dimensionality of the feature set, but also to an apparent lack of data structure. This characteristic limits the applicability of processing techniques, such as wavelet filtering or other filtering techniques that take advantage of known structural relation. On the other hand, it is well known that genes are not expressed independently from other each other: genes have a high interdependence related to the involved regulating biological process.

This thesis aims to improve the current state of the art in microarray classification and to contribute to understand how signal processing techniques can be developed and applied to analyze microarray data. The goal of building a classification framework needs an exploratory work in which algorithms are constantly tried and adapted to the analyzed data. The developed algorithms and classification frameworks in this thesis tackle the problem with two essential building blocks. The first one deals with the lack of a priori structure by inferring a data-driven structure with unsupervised hierarchical clustering tools. The second key element is a proper feature selection tool to produce a precise classifier as an output and to reduce the overfitting risk.

The main focus in this thesis is the binary data classification, field in which we obtained relevant improvements to the state of the art. The first key element is the data-driven structure, obtained by modifying hierarchical clustering algorithms derived from the Treelets algorithm from the literature. Several alternatives to the original reference algorithm have been tested, changing either the similarity metric to merge the feature or the way two feature are merged. Moreover, the possibility to include external sources of information from publicly available biological knowledge and ontologies to improve the structure generation has been studied too. About the feature selection, two alternative approaches have been studied: the first one is a modification of the IFFS algorithm as a wrapper feature selection, while the second approach involved an ensemble learning focus. To obtain good results, the IFFS algorithm has been adapted to the data characteristics by introducing new elements to the selection process like a reliability measure and a scoring system to better select the best feature at each iteration. The second feature selection approach is based on Ensemble learning, taking advantage of the microarryas feature abundance to implement a different selection scheme. New algorithms have been studied in this field, improving state of the art algorithms to the microarray data characteristic of small sample and high feature numbers.

In addition to the binary classification problem, the multiclass case has been addressed too. A new algorithm combining multiple binary classifiers has been evaluated, exploiting the redundancy offered by multiple classifiers to obtain better predictions.

All the studied algorithm throughout this thesis have been evaluated using high quality publicly available data, following established testing protocols from the literature to offer a proper benchmarking with the state of the art.

Whenever possible, multiple Monte Carlo simulations have been performed to increase the robustness of the obtained results.\ 

}, url = {http://hdl.handle.net/10803/145902}, author = {Bosio, M.}, editor = {Salembier, P. and Albert Oliveras} } @conference {cMcGuinness, title = {Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks}, booktitle = {2014 TRECVID Workshop}, year = {2014}, month = {11/2014}, publisher = {National Institute of Standards and Technology (NIST)}, organization = {National Institute of Standards and Technology (NIST)}, address = {Orlando, Florida (USA)}, abstract = {

Insight-DCU participated in the instance search (INS) and semantic indexing (SIN) tasks in 2014. Two very different approaches were submitted for instance search, one based on features extracted using pre-trained deep convolutional neural networks (CNNs), and another based on local SIFT features, large vocabulary visual bag-of-words aggregation, inverted index-based lookup, and geometric verification on the top-N retrieved results. Two interactive runs and two automatic runs were submitted, the best interactive runs achieved a mAP of 0.135 and the best automatic 0.12. Our semantic indexing runs were based also on using convolutional neural network features, and on Support Vector Machine classifiers with linear and RBF kernels. One run was submitted to the main task, two to the no annotation task, and one to the progress task. Data for the no-annotation task was gathered from Google Images and ImageNet. The main task run has achieved a mAP of 0.086, the best no-annotation runs had a close performance to the main run by achieving a mAP of 0.080, while the progress run had 0.043.

[2014 TREC Video Retrieval Evaluation Notebook Papers and Slides]

Poster

}, url = {http://hdl.handle.net/2117/24915}, author = {Kevin McGuinness and Mohedano, Eva and Zhang, ZhenXing and Hu, Feiyan and Albatal, Rami and Gurrin, Cathal and O{\textquoteright}Connor, N. and Smeaton, Alan F. and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Ventura, C.} } @mastersthesis {xPereira, title = {An investigation of eye gaze tracking utilities in image object recognition}, year = {2014}, abstract = {

Computer vision has been one of the most revolutionary technologies of the last few decades. This project investigates how to improve an image recognition system (image classifier) using a not very exploded technology; eye gaze tracking. The aim of this project is to explore the benefits that this technology can bring to an image classifier. The experiment that is set in this project is to build a dataset with an eye tracking device and (using different sized cropped parts of the image based on the eye tracking data) see how the performance of an image classifier is affected with these images. The results are interesting. Since smaller images have to be processed by using this method, the system is more efficient. Regarding the performance, it is very similar to the one obtained without using any eye tracking data, so it is arguable to state that it presents an improvement, and opens new directions of investigation for future works.

Poster


}, author = {Imedio-Pereira, Sergi}, editor = {Mohedano, Eva and Xavier Gir{\'o}-i-Nieto and Albatal, Rami and O{\textquoteright}Connor, N.} } @conference {cMohedano, title = {Object segmentation in images using EEG signals}, booktitle = {ACM Multimedia}, year = {2014}, month = {11/2014}, address = {Orlando, Florida (USA)}, abstract = {

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When an image region, specifically a block of pixels, is displayed we estimate the probability of the block containing the object of interest using a score based on EEG activity. After several such blocks are displayed, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study shows that BCI and simple EEG analysis are useful in locating object boundaries in images.

}, keywords = {Brain-computer interfaces, Electroencephalography, GrabCut algorithm, Interactive segmentation, Object segmentation, rapid serial visual presentation}, doi = {10.1145/2647868.2654896}, url = {http://arxiv.org/abs/1408.4363}, author = {Mohedano, Eva and Healy, Graham and Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cBosio13a, title = {Ensemble learning and hierarchical data representation for microarray classification}, booktitle = {13th IEEE International Conference on BioInformatics and BioEngineering BIBE}, year = {2013}, month = {11/2013}, publisher = {13th IEEE International Conference on BioInformatics and BioEngineering,}, organization = {13th IEEE International Conference on BioInformatics and BioEngineering,}, address = {Chania, Crete}, abstract = {

Microarray data classification is an open and active research field.

The development of more accurate algorithms is of great interest also because many of the techniques can be straightforwardly applied in analyzing different kinds of omics data.

In this work, an ensemble feature selection algorithm is applied within a binary classification framework from [1] that already got good predictive results. Ensemble feature selection is a rich field of research.

Ensemble techniques take some individual experts (i.e. classifiers) to combine them to improve the individual expert results with a voting scheme.

In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote.

Two versions of an ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC [2].

The studied ensemble technique improves the state of the art results by producing classifiers with significantly better results and the proposed key elements have shown to be useful for the prediction performance.

}, keywords = {Microarray classification; metagenes; hierarchical representation; Treelets; feature selection; LDA; ensemble.}, author = {Bosio, M. and Salembier, P. and Albert Oliveras and P. Bellot} } @conference {cBosio13, title = {Hierarchical Clustering Combining Numerical and Biological Similarities for Gene Expression Data Classification}, booktitle = {35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}13) }, year = {2013}, month = {07/2013}, publisher = {IEEE EMBS}, organization = {IEEE EMBS}, address = {Osaka, Japan}, keywords = {Biological interpretation, Hierarchical representation, microarray classification}, author = {Bosio, M. and Salembier, P. and P. Bellot and Albert Oliveras} } @mastersthesis {xMohedano13, title = {Investigating EEG for Saliency and Segmentation Applications in Image Processing}, year = {2013}, abstract = {

Advisors: Kevin McGuinness, Xavier Gir{\'o}-i-Nieto, Noel O{\textquoteright}Connor

School: Dublin City University (Ireland)

The main objective of this project is to implement a new way to compute saliency maps and to locate an object in an image by using a brain-computer interface. To achieve this, the project is centered in designing the proper way to display the different parts of the images to the users in such a way that they generate measurable reactions. Once an image window is shown, the objective is to compute a score based on the EEG activity and compare its result with the current automatic methods to estimate saliency maps. Also, the aim of this work is to use the EEG map as a seed for another segmentation algorithm that will extract the object from the background in an image. This study provides evidence that BCI are useful to find the location of the objects in a simple images via straightforward EEG analysis and this represents the starting point to locate objects in more complex images.

Related post on BitSearch.

Eva Mohedano, "Investigating EEG for Saliency and Segmentation Applications in Image Processing" from Xavi Gir{\'o}
}, keywords = {Brain-computer interfaces (BCI), Eelectroencephalography (EEG), rapid serial visual presentation (RSVP), saliency map, segmentation}, author = {Mohedano, Eva}, editor = {Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @article {aBosio12, title = {Gene Expression Data Classification Combining Hierarchical Representation and Efficient Feature Selection}, journal = {Journal of Biological Systems}, volume = {20}, year = {2012}, pages = {349-375}, abstract = {

A general framework for microarray data classification is proposed in this paper. It produces precise and reliable classifiers through a two-step approach. At first, the original feature set is enhanced by a new set of features called metagenes. These new features are obtained through a hierarchical clustering process on the original data. Two different metagene generation rules have been analyzed, called Treelets clustering and Euclidean clustering. Metagenes creation is attractive for several reasons: first, they can improve the classification since they broaden the available feature space and capture the common behavior of similar genes reducing the residual measurement noise. Furthermore, by analyzing some of the chosen metagenes for classification with gene set enrichment analysis algorithms, it is shown how metagenes can summarize the behavior of functionally related probe sets. Additionally, metagenes can point out, still undocumented, highly discriminant probe sets numerically related to other probes endowed with prior biological information in order to contribute to the knowledge discovery process.

The second step of the framework is the feature selection which applies the Improved Sequential Floating Forward Selection algorithm (IFFS) to properly choose a subset from the available feature set for classification composed of genes and metagenes. Considering the microarray sample scarcity problem, besides the classical error rate, a reliability measure is introduced to improve the feature selection process. Different scoring schemes are studied to choose the best one using both error rate and reliability. The Linear Discriminant Analysis classifier (LDA) has been used throughout this work, due to its good characteristics, but the proposed framework can be used with almost any classifier. The potential of the proposed framework has been evaluated analyzing all the publicly available datasets offered by the Micro Array Quality Control Study, phase II (MAQC). The comparative results showed that the proposed framework can compete with a wide variety of state of the art alternatives and it can obtain the best mean performance if a particular setup is chosen. A Monte Carlo simulation confirmed that the proposed framework obtains stable and repeatable results.

}, doi = {10.1142/S0218339012400025}, url = {http://www.worldscientific.com/doi/abs/10.1142/S0218339012400025}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cBosio12, title = {Microarray classification with hierarchical data representation and novel feature selection criteria}, booktitle = {IEEE 12th International Conference on BioInformatics and BioEngineering}, year = {2012}, month = {11/2012}, address = {Larnaca, Cyprus}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cBosio12a, title = {Multiclass cancer microarray classification algorithm with Pair-Against-All redundancy}, booktitle = {The 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS{\textquoteright}12)}, year = {2012}, month = {12/2012}, address = {Washington, DC, USA}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cBosio11, title = {Feature set enhancement via hierarchical clustering for microarray classification}, booktitle = {IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2011}, year = {2011}, month = {dec.}, pages = {226 -229}, abstract = {

A new method for gene expression classification is proposed in this paper. In a first step, the original feature set is enriched by including new features, called metagenes, produced via hierarchical clustering. In a second step, a reliable classifier is built from a wrapper feature selection process. The selection relies on two criteria: the classical classification error rate and a new reliability measure. As a result, a classifier with good predictive ability using as few features as possible to reduce the risk of overfitting is obtained. This method has been tested on three public cancer datasets: leukemia, lymphoma and colon. The proposed method has obtained interesting classification results and the experiments have confirmed the utility of both metagenes and feature ranking criterion to improve the final classifier.

}, keywords = {cancer, classical classification error rate, classifier, colon, feature set enhancement, gene expression classification, hierarchical clustering, leukemia, lymphoma, medical computing, metagenes, microarray classification, overfitting risk reduction, pattern classification, pattern clustering, public cancer datasets, reliability measure, wrapper feature selection process}, doi = {10.1109/GENSiPS.2011.6169486}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cSchreer11, title = {Format-agnostic approach for production, delivery and rendering of immersive media}, booktitle = {Networked and Electronic Media Summit}, year = {2011}, pages = {{\textendash}}, isbn = {SN}, url = {http://nem-summit.eu/program/}, author = {Schreer, O. and Thomas, G. and Niamut, O. and Macq, J. and Kochale, A. and Batke, J. and Ruiz-Hidalgo, J. and Oldfield, R. and Shirley, B. and Thallinger, G.} } @conference {cBragos11a, title = {Implementation and first results of the Introduction to Engineering course in the ETSETB-UPC new degrees}, booktitle = {II Conferencia Internacional en Fomento e Innovaci{\'o}n con Nuevas Tecnolog{\'\i}as en la Docencia de la Ingenier{\'\i}a}, year = {2011}, pages = {1{\textendash}4}, isbn = {978-1-4577-0559-5}, doi = {10.1109/FINTDI.2011.5945971}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5945971}, author = {Bragos, R. and Pegueroles, J. and Alarc{\'o}n, E. and Camps, A. and Sarda, J. and Consolacion, C. and Mussons, J. and Pons, O. and Albert Oliveras and Garc{\'\i}a, M. and Onrubia, R. and Elisa Sayrol} } @phdthesis {dObrador11, title = {Media Aesthetics Based Multimedia Storytelling}, year = {2011}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

Since the earliest of times, humans have been interested in recording their life experiences, for future reference and for storytelling purposes. This task of recording experiences {\textendash}i.e., both image and video capture{\textendash} has never before in history been as easy as it is today. This is creating a digital information overload that is becoming a great concern for the people that are trying to preserve their life experiences. As high-resolution digital still and video cameras become increasingly pervasive, unprecedented amounts of multimedia, are being downloaded to personal hard drives, and also uploaded to online social networks on a daily basis. The work presented in this dissertation is a contribution in the area of multimedia organization, as well as automatic selection of media for storytelling purposes, which eases the human task of summarizing a collection of images or videos in order to be shared with other people. As opposed to some prior art in this area, we have taken an approach in which neither user generated tags nor comments {\textendash}that describe the photographs, either in their local or on-line repositories{\textendash} are taken into account, and also no user interaction with the algorithms is expected. We take an image analysis approach where both the context images {\textendash}e.g. images from online social networks to which the image stories are going to be uploaded{\textendash}, and the collection images {\textendash}i.e., the collection of images or videos that needs to be summarized into a story{\textendash}, are analyzed using image processing algorithms. This allows us to extract relevant metadata that can be used in the summarization process. Multimedia-storytellers usually follow three main steps when preparing their stories: first they choose the main story characters, the main events to describe, and finally from these media sub-groups, they choose the media based on their relevance to the story as well as based on their aesthetic value. Therefore, one of the main contributions of our work has been the design of computational models {\textendash}both regression based, as well as classification based{\textendash} that correlate well with human perception of the aesthetic value of images and videos. These computational aesthetics models have been integrated into automatic selection algorithms for multimedia storytelling, which are another important contribution of our work. A human centric approach has been used in all experiments where it was feasible, and also in order to assess the final summarization results, i.e., humans are always the final judges of our algorithms, either by inspecting the aesthetic quality of the media, or by inspecting the final story generated by our algorithms. We are aware that a perfect automatically generated story summary is very hard to obtain, given the many subjective factors that play a role in such a creative process; rather, the presented approach should be seen as a first step in the storytelling creative process which removes some of the ground work that would be tedious and time consuming for the user. Overall, the main contributions of this work can be capitalized in three: (1) new media aesthetics models for both images and videos that correlate with human perception, (2) new scalable multimedia collection structures that ease the process of media summarization, and finally, (3) new media selection algorithms that are optimized for multimedia storytelling purposes

}, url = {http://hdl.handle.net/10803/33293}, author = {Obrador, P.}, editor = {Casas, J.} } @inbook {bWaibel10, title = {Computers in the Human Interaction Loop}, booktitle = {Handbook on Ambient Intelligence and Smart Environments (AISE)}, year = {2010}, pages = {1071{\textendash}1116}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA}, abstract = {

It is a common experience in our modern world, for us humans to be overwhelmed by the complexities of technological artifacts around us, and by the attention they demand. While technology provides wonderful support and helpful assistance, it also causes an increased preoccupation with technology itself and a related fragmentation of attention. But as humans, we would rather attend to a meaningful dialog and interaction with other humans, than to control the operations of machines that serve us. The cause for such complexity and distraction, however, is a natural consequence of the flexibility and choice of functions and features that technology has to offer. Thus flexibility of choice and the availability of desirable functions are in conflict with ease of use and our very ability to enjoy their benefits.\ 

}, isbn = {978-0-387-93807-3}, doi = {10.1007/978-0-387-93808-0_40}, author = {Waibel, A. and Stiefelhagen, R. and Carlson, R. and Casas, J. and Kleindienst, J. and Lamel, L. and Lanz, O. and Mostefa, D. and Omologo, M. and Pianesi, F. and Polymenakos, L. and Potamianos, G. and Soldatos, J. and Sutschet, G. and Terken, J.} } @article {cSayrol10, title = {Mixed integration of CDIO skills into telecommunication engineering curricula}, journal = {Elektronika ir elektrotechnika}, volume = {102}, number = {102}, year = {2010}, pages = {127{\textendash}130}, issn = {1392-1215}, url = {http://www.ktu.lt/lt/mokslas/zurnalai/elektros_z/z102/32__ISSN_1392-1215_Mixed\%20Integration\%20of\%20CDIO\%20skills\%20into\%20Telecommunication\%20Engineering\%20Curricula.pdf}, author = {Elisa Sayrol and Bragos, R. and Alarc{\'o}n, E. and Cabrera, M. and Calveras, A. and Comellas, J. and O{\textquoteright}Callaghan, J. and Pegueroles, J. and Pla, E. and Prat, L. and Saez, G. and Sarda, J. and Tallon, C.} } @conference {cBragos10, title = {Proceso de inversi{\'o}n de competencias gen{\'e}ricas en los nuevos planes de estudios de grado de la ETSETB de acuerdo con el modelo CDIO}, booktitle = {IX Congreso de Tecnolog{\'\i}as Aplicadas a la Ense{\~n}anza de la Electr{\'o}nica}, year = {2010}, pages = {1{\textendash}9}, isbn = {978-84-96737-68-6}, url = {http://upcommons.upc.edu/e-prints/urlFiles?idDrac=4451103}, author = {Bragos, R. and Alarc{\'o}n, E. and Cabrera, M. and Calveras, A. and Comellas, J. and O{\textquoteright}Callaghan, J. and Pegueroles, J. and Prat, L. and Saez, G. and Sarda, J. and Elisa Sayrol} } @conference {cOliveras09, title = {Maximum likelihood factor analysis in malaria cytokines analysis and modelling}, booktitle = {IEEE International Workshop on Genomic Signal Processing and Statistics}, year = {2009}, isbn = {84-605-9799-7}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5174320\&arnumber=5174336\&count=57\&index=14}, author = {Albert Oliveras} } @conference {cRolon09, title = {Modeling of contours in wavelet domain for generalized lifting image compression}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009}, year = {2009}, address = {Taipei, Taiwan}, author = {Rol{\'o}n, J. and Ortega, A. and Salembier, P.} } @conference {cCalderero09b, title = {Performance evaluation of probability density estimators for unsupervised information theoretical region merging}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {4397{\textendash}4400}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2009.5413621}, url = {http://hdl.handle.net/2117/9510}, author = {Calderero, F. and Marqu{\'e}s, F. and Ortega, A.} } @article {aOliveras08, title = {Elevated basal hepcidin levels in the liver may inhibit the development of malaria infection: Another piece towards solving the malaria puzzle?}, journal = {Medical hypotheses}, volume = {70}, number = {3}, year = {2008}, pages = {630{\textendash}634}, issn = {0306-9877}, doi = {10.1016/j.mehy.2007.07.021}, url = {http://www.sciencedirect.com/science/article/B6WN2-4PK8B8Y-5/2/f5ccc9584cc2e8c2095731c9be9d4a31}, author = {Albert Oliveras and Espel-Masferrer, E.} } @conference {cCalderero07, title = {Multiple view region matching using as a Lagrangian optimization problem}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, author = {Calderero, F. and Marqu{\'e}s, F. and Ortega, A.} } @conference {cCasas03, title = {Advanced content-based semantic scene analysis and information retrieval: the Schema Project}, booktitle = {4th European Workshop on Image Analysis for Multimedia Interactive Services_x000D_}, year = {2003}, pages = {43{\textendash}45}, abstract = {

The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: contentbased multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic-web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will
be integrated in the SCHEMA module-based, expandable reference system.

}, author = {Izquierdo, E. and Casas, J. and Leonardi, R. and Migliorati, P. and O{\textquoteright}Connor, N. and Kompatsiaris, I. and Strintzis, M.} } @conference {cO{\textquoteright}Connor03, title = {Region and object segmentation algorithms in the Qimera segmentation platform}, booktitle = {Third International Workshop on Content-Based Multimedia Indexing}, year = {2003}, pages = {95{\textendash}103}, abstract = {

In this paper we present the Qimera segmentation platform and describe the different approaches to segmentation that have been implemented in the system to date. Analysis techniques have been implemented for both region-based and object-based segmentation. The region-based segmentation algorithms include: a colour segmentation algorithm based on a modified Recursive Shortest Spanning Tree (RSST) approach, an implementation of a colour image segmentation algorithm based on the K-Means-with-Connectivity-Constraint (KMCC) algorithm and an approach based on the Expectation Maximization (EM) algorithm applied in a 6D colour/texture space. A semi-automatic approach to object segmentation that uses the modified RSST approach is outlined. An automatic object segmentation approach via snake propagation within a level-set framework is also described. Illustrative segmentation results are presented in all cases. Plans for future research within the Qimera project are also discussed.

}, isbn = {978-84-612-2373-2}, author = {O{\textquoteright}Connor, N. and Sav, S. and Adamek, T. and Mezaris, V. and Kompatsiaris, I. and Lui, T. and Izquierdo, E. and Ferran, C. and Casas, J.} } @article {aComas03, title = {Unbalanced multiple description video coding based on a rate-distortion optimization}, journal = {Eurasip Journal on Applied Signal Processing}, volume = {2003}, number = {1}, year = {2003}, pages = {81{\textendash}90}, issn = {1110-8657}, author = {Comas, D. and Singh, R. and Ortega, A. and Marqu{\'e}s, F.} } @article {aSalembier00a, title = {Description Schemes for Video Programs, Users and Devices}, journal = {Signal processing: image communication}, volume = {16}, number = {1}, year = {2000}, pages = {211{\textendash}234}, issn = {0923-5965}, author = {Salembier, P. and Richard, Q. and O{\textquoteright}Connor, N. and Correia, P. and Sezan, I and van Beek, P} } @conference {cSalembier99, title = {The DICEMAN description schemes for still images and video sequences}, booktitle = {Workshop on Image Analysis for Multimedia Application Services, WIAMIS{\textquoteright}99}, year = {1999}, pages = {25{\textendash}34}, address = {Berlin, Germany}, isbn = {84-7653-885-5}, author = {Salembier, P. and O{\textquoteright}Connor, N. and Correa, P. and Ward, L} } @conference {cGasull99, title = {Editor gr{\'a}fico de figuras MATLAB}, booktitle = {III Congreso de Usuarios de MATLAB}, year = {1999}, pages = {219{\textendash}227}, isbn = {84-699-1358-1}, author = {Gasull, A. and Elisa Sayrol and Moreno, A. and Vallverdu, F. and Salavedra, J. and Albert Oliveras} } @conference {cSalembier99a, title = {Hierarchical visual description schemes for still images and video sequences}, booktitle = {1999 IEEE International Conference on Image Processing, ICIP 1999}, year = {1999}, address = {Kobe, Japan}, author = {Salembier, P. and O{\textquoteright}Connor, N. and Correia, P. and Pereira, F.} } @conference {cSayrol99a, title = {Simulaci{\'o}n digital de se{\~n}ales y sistemas anal{\'o}gicos}, booktitle = {III Congreso de Usuarios de MATLAB}, year = {1999}, pages = {67{\textendash}76}, isbn = {84-699-1358-1}, author = {Sayrol E. and Gasull, A. and Moreno, A. and Vallverdu, F. and Salavedra, J. and Albert Oliveras} } @article {aSalembier98, title = {Antiextensive connected operators for image and sequence processing}, journal = {IEEE transactions on image processing}, volume = {7}, number = {4}, year = {1998}, pages = {555{\textendash}570}, abstract = {

This paper deals with a class of morphological operators called connected operators. These operators filter the signal by merging its flat zones. As a result, they do not create any new contours and are very attractive for filtering tasks where the contour information has to be preserved. This paper shows that connected operators work implicitly on a structured representation of the image made of flat zones. The max-tree is proposed as a suitable and efficient structure to deal with the processing steps involved in antiextensive connected operators. A formal definition of the various processing steps involved in the operator is proposed and, as a result, several lines of generalization are developed. First, the notion of connectivity and its definition are analyzed. Several modifications of the traditional approach are presented. They lead to connected operators that are able to deal with texture. They also allow the definition of connected operators with less leakage than the classical ones. Second, a set of simplification criteria are proposed and discussed. They lead to simplicity-, entropy-, and motion-oriented operators. The problem of using a nonincreasing criterion is analyzed. Its solution is formulated as an optimization problem that can be very efficiently solved by a Viterbi (1979) algorithm. Finally, several implementation issues are discussed showing that these operators can be very efficiently implemented.

}, issn = {1057-7149}, doi = {10.1109/83.663500}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=663500}, author = {Salembier, P. and Albert Oliveras and Garrido, L.} } @conference {cGarrido97a, title = {Anti-extensive Connected Operators with Application to Image Sequences}, booktitle = {VII Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1997}, pages = {151{\textendash}156}, address = {Barcelona, Spain}, isbn = {84-922529-0-1}, author = {Garrido, L. and Salembier, P. and Albert Oliveras} } @phdthesis {dOliveras97, title = {Contribuci{\'o} a l{\textquoteright}an{\`a}lisi morfol{\`o}gica d{\textquoteright}imatges amb operadors connexes}, year = {1997}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Albert Oliveras}, editor = {Salembier, P.} } @article {aMeyer97, title = {Morphological tools for segmentation: connected operators and watersheds}, journal = {Annales des t{\'e}lecommunications. Annals of telecommunications}, volume = {52}, number = {7-8}, year = {1997}, pages = {366{\textendash}379}, issn = {0003-4347}, author = {Meyer, F. and Albert Oliveras and Salembier, P. and Vachier, C.} } @conference {cGarrido97, title = {Motion analysis of image sequences using connected operators}, booktitle = {SPIE Visual Communications and Image Processing, VCIP{\textquoteright}97}, year = {1997}, pages = {546{\textendash}557}, address = {San Jose, CA, USA}, isbn = {0-8194-2435-8}, author = {Garrido, L. and Albert Oliveras and Salembier, P.} } @conference {cOliveras97, title = {Stereo image analyis using connected operators}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1997}, year = {1997}, pages = {3169{\textendash}3172}, address = {Munich, Germany}, isbn = {978-989-8109-05-7}, author = {Albert Oliveras and Garrido, L. and Salembier, P.} } @article {pOliveras96, title = {Filtering Method and Corresponding Filtering System}, number = {96402925.0-}, year = {1996}, type = {Invention patent}, author = {Albert Oliveras and Salembier, P. and Garrido, L.} } @conference {cOliveras96, title = {Generalized connected operators}, booktitle = {SPIE Visual Communication and Image Processing, VCIP{\textquoteright}96}, year = {1996}, pages = {2727{\textendash}2771}, address = {Orlando, Florida, USA}, author = {Albert Oliveras and Salembier, P.} } @conference {cSalembier96b, title = {Motion connected operators for image sequences}, booktitle = {VIII European Signal Processing Conference, EUSIPCO{\textquoteright}96}, year = {1996}, pages = {1083{\textendash}1086}, address = {Trieste, Italy}, isbn = {84-600-9597-5}, author = {Salembier, P. and Albert Oliveras and Garrido, L.} } @conference {cOliveras96b, title = {Practical extensions of connected operators}, booktitle = {International Symposium on Mathematical Morphology, ISMM 1996}, year = {1996}, pages = {97{\textendash}110}, address = {Atlanta (GA), USA}, author = {Salembier, P. and Albert Oliveras} } @conference {cOliveras91, title = {Tou - Brazo Robot Asistencial: Control Verbal}, booktitle = {2{\textordmasculine} Congreso de la Asociaci{\'o}n Espa{\~n}ola de Rob{\'o}tica}, year = {1991}, pages = {93{\textendash}100}, isbn = {0-7803-7043-0}, author = {Albert Oliveras and Fuertes, J. and Vill{\`a}, R.} } @conference {cLleida91, title = {Two level c.s.r. using demisyllable..}, booktitle = {EUROPEAN CONF. ON SPEECH COMMUN. AND TECHN. EUROSPEECH{\textquoteright}91}, year = {1991}, pages = {1199{\textendash}1202}, isbn = {10184074}, author = {Lleida, E. and Albert Oliveras and Nadeu, C. and Mari{\~n}o, J.} } @conference {cNadeu90, title = {Simbad:a tool for speech analysis and synthesis}, booktitle = {IASTED INT.CONF.SIGNAL PROC.\&DIG.FILT.}, year = {1990}, isbn = {1522-4880}, author = {Nadeu, C. and Albert Oliveras and Mari{\~n}o, J.} }