@article {aGeleta, title = {Deep Variational Autoencoders for Population Genetics}, year = {Submitted}, abstract = {

Motivation Modern biobanks provide numerous high-resolution genomic sequences of diverse populations. These datasets enable a better understanding of genotype-phenotype interactions with genome-wide association studies (GWAS) and power a new personalized precision medicine with polygenic risk scores (PRS). In order to account for diverse and admixed populations, new algorithmic tools are needed in order to properly capture the genetic composition of populations. Here we explore deep learning techniques, namely variational autoencoders (VAEs), to process genomic data from a population perspective. We hope this work will encourage the adoption of deep neural networks in the population genetics community.

Results In this paper, we show the power of VAEs for a variety of tasks relating to the interpretation, classification, simulation, and compression of genomic data with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the proposed applications with and without ancestry conditioning. The unsupervised setting of autoencoders allows for the detection and learning of granular population structure and inferring of informative latent factors. The learned latent spaces of VAEs are able to capture and represent differentiated Gaussian-like clusters of samples with similar genetic composition on a fine-scale from single nucleotide polymorphisms (SNPs), enabling applications in dimensionality reduction, data simulation, and imputation. These individual genotype sequences can then be decomposed into latent representations and reconstruction errors (residuals) which provide a sparse representation useful for lossless compression. We show that different population groups have differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, its effect on compression across populations, its relation to historical migrations, and we show how to introduce autoencoders into existing compression pipelines.

}, doi = {https://doi.org/10.1101/2023.09.27.558320}, url = {https://www.biorxiv.org/content/10.1101/2023.09.27.558320v1}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @phdthesis {dDuarte, title = {Data and methods for a visual understanding of sign languages}, volume = {PhD}, year = {2022}, type = {International Mention}, abstract = {

Signed languages are complete and natural languages used as the first or preferred mode of communication by millions of people worldwide. However, they, unfortunately, continue to be marginalized languages. Designing, building, and evaluating models that work on sign languages presents compelling research challenges and requires interdisciplinary and collaborative efforts. The recent advances in Machine Learning (ML) and Artificial Intelligence (AI) has the power to enable better accessibility to sign language users and narrow down the existing communication barrier between the Deaf community and non-sign language users. However, recent AI-powered technologies still do not account for sign language in their pipelines. This is mainly because sign languages are visual languages, that use manual and non-manual features to convey information, and do not have a standard written form. Thus, the goal of this thesis is to contribute to the development of new technologies that account for sign language by creating large-scale multimodal resources suitable for training modern data-hungry machine learning models and developing automatic systems that focus on computer vision tasks related to sign language that aims at learning better visual understanding of sign languages.Thus, in Part I, we introduce the How2Sign dataset, which is a large-scale collection of multimodal and multiview sign language videos in American Sign Language. In Part II, we contribute to the development of technologies that account for sign languages by presenting in Chapter 4 a framework called Spot-Align, based on sign spotting methods, to automatically annotate sign instances in continuous sign language. We further present the benefits of this framework and establish a baseline for the sign language recognition task on the How2Sign dataset. In addition to that, in Chapter 5 we benefit from the different annotations and modalities of the How2Sign to explore sign language video retrieval by learning cross-modal embeddings. Later in Chapter 6, we explore sign language video generation by applying Generative Adversarial Networks to the sign language domain and assess if and how well sign language users can understand automatically generated sign language videos by proposing an evaluation protocol based on How2Sign topics and English translation.

}, author = {Amanda Duarte}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @article {aTemprana-Salvador22, title = {DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals - Planification, Implementation and Preliminary Results}, journal = {Diagnostics}, volume = {12}, year = {2022}, month = {03/2022}, chapter = {852}, abstract = {

Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year. We describe the bidding process and the careful planning that was required, followed by swift implementation in stages. The purpose of the DigiPatICS project is to increase patient safety and quality of care, improving diagnosis and the efficiency of processes in the pathological anatomy departments of the ICS through process improvement, digital pathology, and artificial intelligence tools.

}, keywords = {artificial intelligence, computational pathology, deep learning, digital pathology, implementation, LIS, primary diagnosis, telepathology, workflow}, doi = {10.3390/diagnostics12040852}, url = {https://www.mdpi.com/2075-4418/12/4/852}, author = {Jordi Temprana-Salvador and Pau L{\'o}pez-Garc{\'\i}a and Josep Castellv{\'\i} Vives and Llu{\'\i}s de Haro and Eudald Ballesta and Matias Rojas Abusleme and Miquel Arrufat and Ferran Marques and Casas, J. and Carlos Gallego and Laura Pons and Jos{\'e} Luis Mate and Pedro Luis Fern{\'a}ndez and Eugeni L{\'o}pez-Bonet and Ramon Bosch and Salom{\'e} Mart{\'\i}nez and Santiago Ram{\'o}n y Cajal and Xavier Matias-Guiu} } @article {xGiro-i-Nieto19, title = {Deep Learning Representations for All (a.ka. the AI hype)}, year = {2021}, abstract = {

Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.

\ Version 2021

\ Version 2020

}, author = {Xavier Gir{\'o}-i-Nieto} } @conference {cGeleta, title = {Deep variational autoencoders for population genetics: applications in classification, imputation, dimensionality reduction, and novel lossless data compression}, booktitle = {American Society of Human Genetics (ASHG)}, year = {2021}, month = {10/2021}, publisher = {ASHG}, organization = {ASHG}, address = {Virtual}, abstract = {

In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of SNPs as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression.

Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multi-dimensional representation and as classification probabilities providing flexible and interpretable population descriptors.

We train our VAE method with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.

Video from the related BSc thesis at UPC Data Science Engineering (2021):

}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @mastersthesis {xNietoa, title = {Discovery and Learning of Navigation Goals from Pixels in Minecraft}, year = {2021}, abstract = {

Pre-training Reinforcement Learning (RL) agents in a task-agnostic manner has shown promising results. However, previous works still struggle to learn and discover meaningful skills in high-dimensional state-spaces. We approach the problem by leveraging unsupervised skill discovery and self-supervised learning of state representations. In our work, we learn a compact latent representation by making use of variational or contrastive techniques. We demonstrate that both allow learning a set of basic navigation skills by maximizing an information theoretic objective. We assess our method in Minecraft 3D maps with different complexities. Our results show that representations and conditioned policies learned from pixels are enough for toy examples, but do not scale to realistic and complex maps. We also explore alternative rewards and input observations to overcome these limitations.



}, author = {Nieto, Juan Jos{\'e}}, editor = {V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCaselles21, title = {Disentangling neural network structure from the weights space}, year = {2021}, abstract = {

Deep Neural Networks have been used to tackle a wide variety of tasks achieving great performance. However, there is still a lack of knowledge of how the training of these models converge and how weights relate to their properties. In this thesis we investigate the structure of the weight space and try to disentangle its properties. Attention mechanisms are introduced to capture relations among neurons{\textquoteright} weights that help in weight reconstruction, hyper-parameter classification and accuracy prediction. Our approach further has the potential to work with variable input size allowing different network width, depth or even architecture types.

}, author = {Caselles, Pol}, editor = {Sch{\"u}rholt, Konstantin and Borth, Damian and Xavier Gir{\'o}-i-Nieto} } @article {aAbadal, title = {A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 imagery}, journal = {Remote Sensing}, volume = {13}, year = {2021}, month = {2021}, pages = {4547}, abstract = {

There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5\% on IoU and almost 10\% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach.

}, author = {Sa{\"u}c Abadal and Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana} } @phdthesis {dCampos20, title = {Deep Learning that Scales: Leveraging Compute and Data}, year = {2020}, month = {12/2020}, school = {Universitat Polit{\`e}cnica de Catalunya}, address = {Barcelona, Catalonia}, abstract = {

Deep learning has revolutionized the field of artificial intelligence in the past decade. Although the development of these techniques spans over several years, the recent advent of deep learning is explained by an increased availability of data and compute that have unlocked the potential of deep neural networks. They have become ubiquitous in domains such as natural language processing, computer vision, speech processing, and control, where enough training data is available. Recent years have seen continuous progress driven by ever-growing neural networks that benefited from large amounts of data and computing power. This thesis is motivated by the observation that scale is one of the key factors driving progress in deep learning research, and aims at devising deep learning methods that scale gracefully with the available data and compute. We narrow down this scope into two main research directions. The first of them is concerned with designing hardware-aware methods which can make the most of the computing resources in current high performance computing facilities. We then study bottlenecks preventing existing methods from scaling up as more data becomes available, providing solutions that contribute towards enabling training of more complex models. This dissertation studies the aforementioned research questions for two different learning paradigms, each with its own algorithmic and computational characteristics. The first part of this thesis studies the paradigm where the model needs to learn from a collection of examples, extracting as much information as possible from the given data. The second part is concerned with training agents that learn by interacting with a simulated environment, which introduces unique challenges such as efficient exploration and simulation.

}, url = { http://hdl.handle.net/10803/670372}, author = {V{\'\i}ctor Campos}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @article {xGiro-i-Nieto20, title = {Deep Self-Supervised Learning for All}, year = {2020}, abstract = {

Deep neural networks have achieved outstanding results in various applications such as vision, language, audio, speech, or reinforcement learning. These powerful function approximators typically require large amounts of data to be trained, which poses a challenge in the usual case where little labeled data is available. During the last year, multiple solutions have been proposed to leverage this problem, based on the concept of self-supervised learning, which can be understood as a specific case of unsupervised learning. This talk will cover its basic principles and provide examples in the field of multimedia.



Deep Self-supervised Learning for All - Xavier Giro - X-Europe 2020 from Universitat Polit{\`e}cnica de Catalunya
}, url = {https://deep-self-supervised-learning.carrd.co/}, author = {Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xBalibrea19, title = {Deep learning for semantic segmentation of airplane hyperspectral imaging}, year = {2019}, abstract = {

Given their success, both qualitative and quantitative, Deep Neural Networks have been used to approach classification and segmentation problems for images, especially during these last few years where it has been possible to design computers with sufficient capacity to make quick and efficient experiments.

In this work, we will study the use of two Convolutional Neural Networks (CNNs) to segment the ground of a land section of Maspalomas{\textquoteright} Park using an image taken by the flight of an airplane.

The comparison will be made in terms of computational cost, complexity and results that will be obtained while testing different algorithms, loss functions or optimizers and also while tuning some other parameters. The results will also be compared with a past work done with the same dataset but another methodology (SVM).

}, author = {Mar Balibrea}, editor = {Luis Salgueiro and Ver{\'o}nica Vilaplana} } @article {x19, title = {Deep Learning Representations for All (a.k.a. the AI hype)}, year = {2019}, abstract = {

Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.

Deep Learning Representations for All (a.ka. the AI hype) from Universitat Polit{\`e}cnica de Catalunya
} } @article {aLin19, title = {Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network}, journal = {Sensors}, volume = {19}, year = {2019}, month = {04/2019}, chapter = {1795}, abstract = {

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.

}, keywords = {depth estimation; semantic segmentation; convolutional neural networks; hybrid architecture}, issn = {1424-8220}, doi = {10.3390/s19081795}, url = {https://www.mdpi.com/1424-8220/19/8/1795}, author = {X. Lin and D. Sanchez-Escobedo and Casas, J. and M. Pard{\`a}s} } @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 {cCombaliab, title = {Digitally Stained Confocal Microscopy through Deep Learning}, booktitle = {International Conference on Medical Imaging with Deep Learning (MIDL 2019)}, year = {2019}, month = {07/2019}, address = {London}, abstract = {

Specialists have used confocal microscopy in the ex-vivo modality to identify tumors with\ an overall sensitivity of 96.6\% and specicity of 89.2\%. However, this technology hasn{\textquoteright}t\ established yet in the standard clinical practice because most pathologists lack the knowledge to interpret its output. In this paper we propose a combination of deep learning and\ computer vision techniques to digitally stain confocal microscopy images into H\&E-like\ slides, enabling pathologists to interpret these images without specic training. We use a\ fully convolutional neural network with a multiplicative residual connection to denoise the\ confocal microscopy images, and then stain them using a Cycle Consistency Generative\ Adversarial Network.

}, author = {Marc Combalia and Javiera P{\'e}rez-Anker and Adriana Garc{\'\i}a-Herrera and Ll{\'u}cia Alos and Ver{\'o}nica Vilaplana and Ferran Marques and Susana Puig and Josep Malvehy} } @unpublished {xRamon, title = {Deep Learning algorithms for 3D Reconstruction and Simulation of Aesthetic Procedures}, year = {2018}, month = {07/2018}, type = {Phd Thesis Proposal}, abstract = {

3D technology is key for a wide range of industries. Medicine, construction, cinema and many other disciplines can nowadays digitalize the world we perceive using 3D reconstruction algorithms, create new objects by means of 3D printers or analyze the world using 3D detection and segmentation techniques.

These tools are transforming how research and industry problems are addressed. Concretely, in the field of aesthetic surgery, a fluent communication between doctors and patients is crucial in order to maximize the success of the operatories and the satisfaction of the patients. A new trend in the industry is to incorporate 3D technology during the consultation, with the goa lof improving that communication. By reconstructing the body of the patient and simulating aesthetic procedures on it, he or she can realise how a concrete modification would look like when applied to his or her body. It is also beneficial for the physicians, since they can measure the effectiveness of the applied treatments and also convert more consultations into operations due to an increase of confidence in the patient.

This thesis proposal defines the research directions to follow during an industrial doctorate to be developed in Crisalix Labs, in collaboration of the Image Processing Group at the Universitat Polit{\`e}cnica de Catalunya. Industrial doctorates are promoted from the Government of Catalonia to promote the transfer of knowledge from universities to local industry as an element for innovation and technical excellence.

}, author = {Ramon, Eduard}, editor = {Xavier Gir{\'o}-i-Nieto} } @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 {cBellvera, title = {Detection-aided liver lesion segmentation using deep learning}, booktitle = {ML4H: Machine Learning for Health Workshop at NIPS 2017}, year = {2017}, month = {11/2017}, abstract = {

A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments. In this work we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs), that have proven good results in a variety of computer vision tasks, including medical imaging. The network that segments the lesions consists of a cascaded architecture, which first focuses on the region of the liver in order to segment the lesions on it. Moreover, we train a detector to localize the lesions, and mask the results of the segmentation network with the positive detections. The segmentation architecture is based on DRIU, a Fully Convolutional Network (FCN) with side outputs that work on feature maps of different resolutions, to finally\  benefit from the multi-scale information learned by different stages of the network. The main contribution of this work is the use of a detector to localize the lesions, which we show to be beneficial to remove false positives triggered by the segmentation network.

Detection-aided liver lesion segmentation using deep learning from Xavier Giro-i-Nieto
}, author = {M{\'\i}riam Bellver and Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Jordi Torres and Xavier Gir{\'o}-i-Nieto and Luc van Gool} } @mastersthesis {xBellvera, title = {Detection-aided medical image segmentation using deep learning}, year = {2017}, abstract = {

Program:\ Master{\textquoteright}s Degree in Telecommunications Engineering

Grade: A with honours (10.0/10.0)

A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and also to assess the response to the according treatments. In this thesis we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans, as well as other anatomical structures and organs of the human body. We have used Convolutional Neural Networks (CNNs), that have proven good results in a variety of tasks, including medical imaging. The network to segment the lesions consists of a cascaded architecture, which first focuses on the liver region in order to segment the lesion. Moreover, we train a detector to localize the lesions and just keep those pixels from the output of the segmentation network where a lesion is detected. The segmentation architecture is based on DRIU [24], a Fully Convolutional Network (FCN) with side outputs that work at feature maps of different resolutions, to finally benefit from the multi-scale information learned by different stages of the network. Our pipeline is 2.5D, as the input of the network is a stack of consecutive slices of the CT scans. We also study different methods to benefit from the liver segmentation in order to delineate the lesion. The main focus of this work is to use the detector to localize the lesions, as we demonstrate that it helps to remove false positives triggered by the segmentation network. The benefits of using a detector on top of the segmentation is that the detector acquires a more global insight of the healthiness of a liver tissue compared to the segmentation network, whose final output is pixel-wise and is not forced to take a global decision over a whole liver patch. We show experiments with the LiTS dataset for the lesion and liver segmentation. In order to prove the generality of the segmentation network, we also segment several anatomical structures from the Visceral dataset.

Detection-aided liver lesion segmentation using deep learning from Xavier Giro-i-Nieto
}, author = {M{\'\i}riam Bellver}, editor = {Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Luc van Gool and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @conference {cLina, title = {Disentangling Motion, Foreground and Background Features in Videos}, booktitle = {CVPR 2017 Workshop Brave New Motion Representations}, year = {2017}, month = {05/2017}, abstract = {

This paper instroduces an unsupervised framework to extract semantically rich features for video representation. Inspired by how the human visual system groups objects based on motion cues, we propose a deep convolutional neural network that disentangles motion, foreground and background information. The proposed architecture consists of a 3D convolutional feature encoder for blocks of 16 frames, which is trained for reconstruction tasks over the first and last frames of the sequence. The model is trained with a fraction of videos from the UCF-101 dataset taking as ground truth the bounding boxes around the activity regions. Qualitative results indicate that the network can successfully update the foreground appearance based on pure-motion features. The benefits of these learned features are shown in a discriminative classification task when compared with a random initialization of the network weights, providing a gain of accuracy above the 10\%.

Disentangle motion, Foreground and Background Features in Videos from Xavier Giro-i-Nieto
}, author = {Lin, Xunyu and V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Cristian Canton-Ferrer} } @conference {c, title = {Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster}, booktitle = {International Conference on Computational Science (ICCS)}, year = {2017}, month = {06/2017}, publisher = {Elsevier}, organization = {Elsevier}, address = {Zurich, Switzerland}, abstract = {

Deep learning algorithms base their success on building high learning capacity models with millions of parameters that are tuned in a data-driven fashion. These models are trained by processing millions of examples, so that the development of more accurate algorithms is usually limited by the throughput of the computing devices on which they are trained. In this work, we explore how the training of a state-of-the-art neural network for computer vision can be parallelized on a distributed GPU cluster. The effect of distributing the training process is addressed from two different points of view. First, the scalability of the task and its performance in the distributed setting are analyzed. Second, the impact of distributed training methods on the final accuracy of the models is studied.

[ICCS 2017 website][Related session in ICCS 2017][Paper in UPCommons]

}, keywords = {distributed computing; parallel systems; deep learning; Convolutional Neural Networks}, doi = {https://doi.org/10.1016/j.procs.2017.05.074}, url = {http://www.sciencedirect.com/science/article/pii/S1877050917306129}, author = {V{\'\i}ctor Campos and Sastre, Francesc and Yag{\"u}es, Maurici and M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @article {aMaceira, title = {Depth map compression via 3D region-based representation}, journal = {Multimedia Tools and Applications}, year = {2016}, month = {07/2016}, abstract = {

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-016-3727-1

In 3D video, view synthesis is used to create new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a new 3D plane representation of the scene is presented which improves the performance of current standard video codecs in the view synthesis domain. Two image segmentation algorithms are proposed for generating a color and depth segmentation. Using both partitions, depth maps are segmented into regions without sharp discontinuities without having to explicitly signal all depth edges. The resulting regions are represented using a planar model in the 3D world scene. This 3D representation allows an ecient encoding while preserving the 3D characteristics of the scene. The 3D planes opens up the possibility to code multiview images with an unique representation.

}, keywords = {3D representation, Data Compression, Depth map coding, Image segmentation}, doi = {10.1007/s11042-016-3727-1}, url = {http://rdcu.be/nqyE}, author = {Maceira, M. and Morros, J.R. and Ruiz-Hidalgo, J.} } @mastersthesis {xCarne-Herrera16, title = {Detect Snap Points in Egocentric Images with Physiological Signals}, year = {2016}, abstract = {

This project addresses a novel problem that has appears in the last years. The use of egocentric cameras, devices that takes images of what we see are growing and the main problem of this images are big data (because at the end of the day, we can have a thousand of images, some of them similar and sometimes with a bad quality or low information) and image retrieval (due to the big data, find a certain moment are very difficult and if we don{\textquoteright}t avoid that problem, the properties of egocentric images became useless).

This work have two objectives: the first one explore images that have physiological signals associated in order to allow us to add some physiological features for the retrieval instead of only base the retrieval in visual features as the actual state of the art. For this part we associate interesting images to the images that are memorable, so a correlation between memorability and physiological signals will be found. The second objective is to deal with the egocentric paradigm. Some recent works shows that machine learning algorithms that have been trained with human-taken images cannot be extended to egocentric images due to the image construction. Based on MIT (Massachusetts Institute of Technology) previous work I built a visual game that allows me to manually annotate the memorability of the images with a simple user interaction (the user don{\textquoteright}t know that he is annotating images during the game). From this game I have computed memorability score and I have obtain predicted scores from a convolutional neural network that MIT present in his work: MemNet. From both results I have compared the results in order to decide if the application of the algorithms on egocentric images is possible.

}, author = {Carn{\'e}-Herrera, Marc}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @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.} } @mastersthesis {xAlonso15, title = {Detecci{\'o} de Text utilitzant Xarxes Neuronals Convolucionals }, year = {2015}, abstract = {

Text detection in images is a challenging problem that has received a significant amount of attention. The inclusion of machine learning allows building accurate classifiers. The goal of this work is to detect text in images. In order to achieve this goal we analyze the Convolutional Neural Network (CNN). We use an implemented CNN to detect areas where there may be text. Then we analyze and filter this areas so we can obtain the detected text.\ 

}, author = {Antea Herrera Alonso}, editor = {Ver{\'o}nica Vilaplana} } @conference {cCampos-Camunez, title = {Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction}, booktitle = {1st International Workshop on Affect and Sentiment in Multimedia}, year = {2015}, month = {10/2015}, publisher = {ACM}, organization = {ACM}, address = {Brisbane, Australia}, abstract = {

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.

Acceptance rate at the ASM workshop{\textquoteright}15: 56\% (9/16) [source]

Diving deep into sentiment: Understanding fine-tuned CNNs for visual sentiment prediction from Xavier Giro

Visual Sentiment Detection by Deep CNNs $\#$acmmm15 @DocXavi @mad_astronaut @brendanjou - great paper/analysis! pic.twitter.com/RcGchcPA9f

{\textemdash} Damian Borth (@damianborth) October 30, 2015
}, doi = {10.1145/2813524.2813530}, url = {http://arxiv.org/abs/1508.05056}, author = {V{\'\i}ctor Campos and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Jou, Brendan} } @conference {cTacchini, title = {Do you have a Pop face? Here is a Pop song. Using profile pictures to mitigate the cold-start problem in Music Recommender Systems}, booktitle = {9th ACM Conference on Recommender Systems}, year = {2015}, month = {09/2015}, author = {Tacchini, E. and Morros, J.R. and Ver{\'o}nica Vilaplana and Sa{\~n}oso, E.} } @article {aPaloua, title = {Depth order estimation for video frames using motion occlusions}, journal = {IET Computer Vision}, volume = {8}, year = {2014}, month = {04/2014}, pages = {152-160}, doi = {10.1049/iet-cvi.2012.0287}, author = {Palou, G. and Salembier, P.} } @article {aSuau13, title = {Detecting End-Effectors on 2.5D data using Geometric Deformable Models: Application to Human Pose Estimation}, journal = {Computer Vision and Image Understanding (CVIU)}, volume = {117}, year = {2013}, chapter = {281{\textendash}288}, abstract = {

End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body.

}, doi = {10.1016/j.cviu.2012.11.006}, url = {http://www.sciencedirect.com/science/article/pii/S1077314212001907}, author = {Suau, X. and Ruiz-Hidalgo, J. and Casas, J.} } @conference {cMaceira12, title = {Depth map coding based on a optimal hierarchical region representation}, booktitle = {3DTV Conference}, year = {2012}, month = {10/2012}, publisher = {IEEE}, organization = {IEEE}, address = {Zurich, Switzerland}, abstract = {

Multiview color information used jointly with depth maps is a widespread technique for 3D video. Using\ this depth information, 3D functionalities such as free view point video can be provided by means of\ depth-image-based rendering techniques. In this paper, a new technique to encode depth maps is\ proposed. Based on the usually smooth structure and the sharp edges of depth map, our proposal segments\ the depth map into homogeneous regions of arbitrary shape and encodes the contents of these regions\ using different texture coding strategies. An optimal lagrangian approach is applied to the hierarchical\ region representation provided by our segmentation technique. This approach automatically selects the\ best encoding strategy for each region and the optimal partition to encode the depth map. To avoid the\ high coding costs of coding the resulting partition, a prediction is made using the associated decoded\ color image.

}, keywords = {3DTV, Depth map coding, depth/texture compresion, rate-distortion optimization, Shape-adaptive DCT}, isbn = {978-1-4673-4903-1}, doi = {10.1109/3DTV.2012.6365481}, author = {Maceira, M. and Ruiz-Hidalgo, J. and Morros, J.R.} } @conference {cPalou12b, title = {Depth Ordering on Image Sequences Using Motion Occlusions}, booktitle = {IEEE Int. Conf. in Image Processing, ICIP 2012}, year = {2012}, month = {09/2012}, address = {Orlando, Florida, USA}, author = {Palou, G. and Salembier, P.} } @article {aCuadras12, title = {Distance-based measures of association with applications in relating hyperspectral images}, journal = {Communications in Statistics - Theory and Method}, volume = {41}, year = {2012}, pages = {2342{\textendash}2355}, abstract = {

We propose a distance-based method to relate two data sets. We define and study some measures of multivariate association based on distances between observations. The proposed approach can be used to deal with general data sets (e.g., observations on continuous, categorical or mixed variables). An application, using Hellinger distance, provides the relationships between two regions of hyperspectral images.

}, issn = {0361-0918}, doi = {10.1080/03610926.2012.654880}, author = {Cuadras, C. and Valero, S. and Cuadras, D. and Salembier, P. and Chanussot, J.} } @conference {cGiro-i-Nieto11, title = {Diversity ranking for video retrieval from a broadcaster archive}, booktitle = {1st ACM International Conference on Multimedia Retrieval (ICMR {\textquoteright}11)}, year = {2011}, pages = {1{\textendash}8}, abstract = {

Video retrieval through text queries is a very common practice in broadcaster archives. The query keywords are compared to the metadata labels that documentalists have previously associated to the video assets. This paper focuses on a ranking strategy to obtain more relevant keyframes among the top hits of the results ranked lists but, at the same time, keeping a diversity of video assets. Previous solutions based on a random walk over a visual similarity graph have been modified to increase the asset diversity by filtering the edges between keyframes depending on their asset. The random walk algorithm is applied separately for ever visual feature to avoid any normalization issue between visual similarity metrics. Finally, this work evaluates performance with two separate metrics: the relevance is measured by the Average Precision and the diversity is assessed by the Average Diversity, a new metric presented in this work.

}, isbn = {978-1-4503-0336-1}, doi = {10.1145/1991996.1992052}, url = {http://dx.doi.org/10.1145/1991996.1992052}, author = {Xavier Gir{\'o}-i-Nieto and Alfaro, M. and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto10, title = {Digimatge, a Rich Internet Application for video retrieval from a Multimedia Asset Management system}, booktitle = {11th ACM SIGMM International Conference on Multimedia Information Retrieval}, year = {2010}, abstract = {

This paper describes the integration of two new services aimed at assisting into the retrieval of video content from an existing Multimedia Asset Manager (MAM) of a TV broadcaster archive. The first tool suggests tags after a first textual query, and the second ranks the keyframe of retrieved assets according to their visual similarity. Both applications were integrated as web services that are accessed from a Rich Internet Application via REST calls.

}, isbn = {978-1-60558-815-5}, doi = {10.1145/1743384.1743458}, url = {http://upcommons.upc.edu/e-prints/urlFiles?idDrac=2249267}, author = {Xavier Gir{\'o}-i-Nieto and Salla, R. and Vives, X.} } @conference {cAsteriadis07, title = {Detection of facial characteristics based on edge information}, booktitle = {International Conference on Computer Vision Theory and Applications, VISAPP 2007}, year = {2007}, pages = {247{\textendash}250}, isbn = {978-972-8865-74-0}, author = {Asteriadis, S. and Nikolaidis, N. and Pitas, I. and M. Pard{\`a}s} } @book {eAguilar07, title = {Diccionari de Telecomunicacions}, year = {2007}, isbn = {978-84-412-1459-0}, author = {Aguilar, M. and Alcober, J. and Altes, J. and Aragones, X. and Artigas, D. and Bardes, D. and Barlabe, A. and Bragos, R. and Calderer, J. and Cardama, A. and Casademont, J. and Casals, L. and Comer{\'o}n, A. and Cotrina, J. and Cruz, L. and Dios, V. and Duxans, H. and Esparza, O. and Esquerra, I. and Garcia, D. and Garcias, P. and Gomez, C. and Gorricho, J. and Guinjoan, F. and Hesselbach, X. and Liria, A. and Lopez, J. and Madrenas, J. and Madue{\~n}o, M. and Mestre, F. and Monte, E. and Morros, J.R. and Mu{\~n}oz, J. and Pallar{\'e}s, E. and Pons, J. and Recolons, J. and Rincon, D. and Riu, P. and Pradell, L. and Pascual-Iserte, A. and Prat, L. and Rey, F. and Villares, J.} } @phdthesis {dMarcello06, title = {Desarrollo de t{\'e}cnicas de procesado de im{\'a}genes, multitemporales y multisensoriales, de teledetecci{\'o}n para la detecci{\'o}n y seguimiento de estructuras oceanogr{\'a}ficas}, year = {2006}, school = {Universidad de Las Palmas de Gran Canaria (ULPGC)}, type = {phd}, author = {Marcello, J.}, editor = {Marqu{\'e}s, F. and F. Eugenio} } @phdthesis {dCorrea06, title = {Dual morphology-based and Bayesian approach for markerless human motion capture in natural interaction environments}, year = {2006}, school = {Universit{\'e} Catholique de Louvain (UCL)}, type = {phd}, url = {http://hdl.handle.net/2078.1/5033}, author = {Correa, P.}, editor = {Marqu{\'e}s, F. and Macq, B.} } @conference {cGiro-i-Nieto05, title = {Detection of Semantic Objects using Description Graphs}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, address = {Genova, Italy}, abstract = {

This paper presents a technique to detect instances of classes (objects) according to their semantic definition in the form of a description graph. Classes are defined as combinations of instances of lower level semantic classes and allow the definition of a semantic tree that organizes classes in semantic levels. At the bottom level of the semantic tree, classes are defined by a perceptual model containing a list of low-level descriptors. The proposed detection algorithm follows a bottom-up/top-down approach, building semantic trees on a region-based representation of the media. The flexibility of the approach is assessed on different examples of planar objects, such as frontal faces, groups of islands, flags and traffic signs.

}, keywords = {Detection algorithms, Explosions, Face detection, Image databases, Indexing, MPEG 7 Standard, Object detection, Testing, Traffic control, Tree graphs}, isbn = {0-7803-9135-7}, doi = {10.1109/ICIP.2005.1529972}, url = {http://dx.doi.org/10.1109/ICIP.2005.1529972}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cSayrol05, title = {Development of a platform offering video copyright protection and security against illegal distribution}, booktitle = {Security, Steganography, and Watermarking of Multimedia Contents}, year = {2005}, pages = {76{\textendash}83}, isbn = {9963-607-06-3}, author = {Elisa Sayrol and Soriano, M. and Fernandez, M. and Casanelles, J. and Tom{\`a}s, J.} } @conference {cBroquetas04, title = {Definici{\'o} d{\textquoteright}un Master Internacional de Recerca: la proposta del Departament de Teoria del Senyal i Comunicacions}, booktitle = {Jornada de reflexi{\'o}n y trabajo sobre el modelo docente de la UPC en el Espacio Europeo de ecuaci{\'o}n Superior (EEES)}, year = {2004}, pages = {1{\textendash}3}, author = {Broquetas, A. and Hernando, J. and Marqu{\'e}s, F. and Romeu, J.} } @conference {cGiro-i-Nieto03, title = {Detection of Semantic Entities using Description Graphs}, booktitle = {4th European Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)}, year = {2003}, month = {04/2003}, address = {London, England}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @inbook {bSalembier02b, title = {Description of a Single Multimedia Document}, booktitle = {Introduction to the mpeg-7: multimedia content description interface}, year = {2002}, pages = {111{\textendash}138}, publisher = {Wiley}, organization = {Wiley}, edition = {B. S. Manjunath, P. Salembier, T. Sikora (Eds.)}, chapter = {8}, isbn = {0471486787}, author = {Benitez, A.B and Martinez, J.M and Rising, H and Salembier, P.} } @conference {cVidal00, title = {Data Hidding in Color Images using Perceptual Models}, booktitle = {COST 254 Intelligent Processing and Facilities for Communications Terminals}, year = {2000}, pages = {21{\textendash}25}, author = {Vidal, J. and Elisa Sayrol and Maribel, M.} } @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 {cBroquetas00, title = {Detecci{\'o}n de vertidos de petr{\'o}leo en el mar a partir de im{\'a}genes SAR}, booktitle = {XV Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {2000}, pages = {327{\textendash}328}, isbn = {84-600-9597-5}, author = {Broquetas, A. and Toronjo, C. and Gasull, A. and F{\'a}bregas, F.X. and Moreno, V. and Herrero, M.} } @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 {cCasas91, title = {Dise{\~n}o de filtros de imagen con funciones de transferencia}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {965{\textendash}969}, isbn = {84-600-7766-7}, author = {Casas, J. and Torres, L.} } @conference {cGasull88, title = {Detecci{\'o}n autom{\'a}tica de contornos en im{\'a}genes ecogr{\'a}ficas}, booktitle = {III Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1988}, pages = {1{\textendash}4}, isbn = {?}, author = {Gasull, A. and Marqu{\'e}s, F. and Sallent, S. and Torres, L. and Vidal, J.} } @article {pSalembier88, title = {Dispositif de R{\'e}ception de Signaux Num{\'e}riques Cod{\'e}s et Modul{\'e}s en Fr{\'e}quences}, number = {8808919-}, year = {1988}, type = {Invention patent}, author = {Salembier, P. and Lamnabhi, M.} } @article {pSalembier87, title = {Dispositif d{\textquoteright}Am{\'e}lioration du D{\'e}codage de Signaux Num{\'e}riques lors de Transmission en Modulation de Fr{\'e}quence}, number = {8710580-}, year = {1987}, type = {Invention patent}, author = {Salembier, P. and Lamnabhi, M.} }