Veronica Vilaplana

Positionsort ascending e-mail
Associate Professor veronica.vilaplana@upc.edu
Office Phone
CN: D5-118 / ESEIAAT: TR2-102 +34 934 017 052
+34 937 398 979
Journals Books Conferences Theses Other Projects Research Demos Teaching

 

Biography

Veronica Vilaplana holds a MSc degree in Mathematics and a MSc degree in Computer Sciences from the Universidad de Buenos Aires (Argentina), and a PhD in Signal Theory and Communications from the Universitat Politècnica de Catalunya (UPC). Since 2002 she is associate professor at the Department of Signal Theory and Communications (UPC). Her current research interests focus on deep learning and other machine learning models for biomedical and remote sensing applications. 

Scientific IDs:

Google Scholar,     ORCID: 0000-0001-6924-9961
Scopus Author ID: 23394280500,     Researcher ID: O-1726-2014,     UPC Futur 

LinkedIn,      ResearchGate

Journal Articles top

In Press
A. Casamitjana, Petrone, P., Molinuevo, J. Luis, Gispert, J. D., and Vilaplana, V., “Projection to Latent Spaces disentangles pathological effects on brain morphology in the asymptomatic phase of Alzheimer’s disease”, Frontiers in Neurology, section Applied Neuroimaging, In Press.
L. Salgueiro, Marcello, J., and Vilaplana, V., “Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks”, Remote Sensing, In Press.
2020
A. Casamitjana, Vilaplana, V., Puch, S., Aduriz, A., Lopez, C., Operto, G., Cacciaglia, R., Falcon, C., Molinuevo, J. L., and Gispert, J. D., “NeAT: a nonlinear analysis toolbox for neuroimaging”, Neuroinformatics, 2020.
J. Gené-Mola, Sanz, R., Rosell-Polo, J. R., Morros, J. R., Ruiz-Hidalgo, J., Vilaplana, V., and Gregorio, E., “Fuji-SfM dataset: A collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry”, vol. Data in Brief, no. Vol. 30, 2020.
J. Gené-Mola, Sanz, R., Rosell-Polo, J. R., Morros, J. R., Ruiz-Hidalgo, J., Vilaplana, V., and Gregorio, E., “Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry”, Computers and Electronics in Agriculture, vol. 169, 2020.
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Book Chapters and Bookstop

2018
M. Combalia and Vilaplana, V., “Monte-Carlo Sampling Applied to Multiple Instance Learning for Histological Image Classification”, in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer International Publishing, 2018, pp. 274-281.
M. Górriz, Aparicio, A., Raventós, B., Vilaplana, V., Sayrol, E., and López-Codina, D., “Leishmaniasis Parasite Segmentation and Classification Using Deep Learning”, in Articulated Motion and Deformable Objects, vol. 10945, Springer International Publishing, 2018, pp. 53-62.
A. Casamitjana, Vilaplana, V., Petrone, P., Molinuevo, J. Luis, and Gispert, J. D., “Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer’s Disease”, in PRedictive Intelligence in MEdicine, vol. 11121, Springer International Publishing, 2018, pp. 60-67.
A. Casamitjana, Catà, M., Sánchez, I., Combalia, M., and Vilaplana, V., “Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation”, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017, Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds)., vol. 10670, Cham: Springer, 2018, pp. 381-391.
2017
A. Casamitjana, Puch, S., Aduriz, A., and Vilaplana, V., “3D Convolutional Neural Networks for Brain Tumor Segmentation: a comparison of multi-resolution architectures”, in Lecture Notes in Computer Vision, vol. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, 2017, pp. 150-161.
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Conference Papers top

In Press
E. Moliner, Salgueiro, L., and Vilaplana, V., “Weakly Supervised Semantic Segmentation for Remote Sensing Hyperspectral Imaging”, in International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), In Press.
M. Combalia, Hueto, F., Puig, S., Malvehy, J., and Vilaplana, V., “Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification”, in CVPR 2020, ISIC Skin Image Analysis Workshop, In Press.
2019
A. Casamitjana, Petrone, P., Falcon, C., Artigues, M., Operto, G., Cacciaglia, R., Molinuevo, J. L., Vilaplana, V., and Gispert, J. D., “Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI”, in Alzheimer's Association International Conference, 2019.
A. Isart, Espasa, M., Vilaplana, V., and Sayrol, E., “CNN-based bacilli detection in sputum samples for tuberculosis diagnosis”, in International Symposium on Biomedical Imaging (ISBI 2019), 2019.
N. Gullón and Vilaplana, V., “Retinal lesions segmentation using CNNs and adversarial training”, in International Symposium on Biomedical Imaging (ISBI 2019), 2019.
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Theses top

2019
A. Casamitjana, “Study of early stages of Alzheimer’s disease using magnetic resonance imaging”, Universitat Politècnica de Catalunya, Barcelona, 2019.
2016
C. Ventura, “Visual Object Analysis using Regions and Local Features”, 2016. (2.5 MB)
2010
V. Vilaplana, “Region-based face detection, segmentation and tracking. framework definition and application to other objects”, Universitat Politècnica de Catalunya (UPC), 2010.

Other top

2019
M. Balibrea, “Deep learning for semantic segmentation of airplane hyperspectral imaging”. 2019. Ms Thesis
L. Tarrés, “Clasificación de lesiones de piel con un ensemble de redes neuronales residuales”. 2019. Ms Thesis
T. Domenech, “Clasificación de imágenes dermatoscópicas utilizando Redes Neuronales Convolucionales e información de metadatos”. 2019. Ms Thesis
R. Casals, “Synthesis of acne images for data augmentation with generative adversarial networks”. 2019. Ms Thesis
P. Domingo, “Interpretability of Deep Learning Models”. 2019. Ms Thesis
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Projects top

A European AI On Demand Platform and Ecosystem European Jan
2019 		Dec
2021
MALEGRA - Multimodal Signal Processing and Machine Learning on Graphs National Jan
2017 		Jun
2021
coach4acne Other Nov
2017 		Mar
2018
BigGraph - Heterogeneous information and graph signal processing for the Big Data era. Application to high-throughput, remote sensing, multimedia and human computer interfaces. National Jan
2014 		Dec
2017
Camomile - Collaborative Annotation of multi-MOdal, MultI-Lingual and multi-mEdia documents European Feb
2013 		Aug
2017
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Research Areas top

Biomedical Applications Internal Jan
2012 		Dec
2020
Region-based image and video processing Internal Jan
1992 		Dec
2020
Deep learning Internal Jun
2014 		Dec
2020
Saliency prediction Internal Feb
2015 		Dec
2019
Multimedia Retrieval Internal Sep
2001 		Dec
2018

Demos and Resources top

Additional results for "Action Tube Extraction based 3D-CNN for RGB-D Action Recognition" Results May
2018
VNeAT (Voxel-wise Neuroimaging Analysis Toolbox) Software Jun
2017
Saliency Maps on Image Hierarchies Results May
2015
Automatic Keyframe Selection over TVC database Results Jun
2013

Teaching top

Acronym Title Level College
CM Codificació Multimèdia Degree in AudioVisual Systems ESEIAAT
VC Computer Vision Degree in Engineering of Audiovisual Systems ESEIAAT
DLAI Deep Learning for Artificial Intelligence Master MET ETSETB TelecomBCN
PDI Digtal Image Processing Degree in Engineering of Audiovisual Systems Escola d'Enginyeria de Terrassa, EET
IPSAV Introduction to Audiovisual Signal Processing Degree in Engineering of Audiovisual Systems TelecomBCN, ETSETB
ICV Introduction to Computer Vision Master in Telecommunications Engineering (MET) ETSETB - Telecom BCN
IDL Introduction to Deep Learning BSc ETSETB TelecomBCN
IHCV M1. Introduction to Humand and Computer Vision Master in Computer Vision (MCV) UAB, UOC, UPC & UPF
CLP Pattern recognition and machine learning Degree in Telecommunications Technologies and Services Engineering Telecom BCN - ETSETB