Veronica Vilaplana


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,     ORCID0000-0001-6924-9961
Scopus Author ID: 23394280500,     Researcher ID: O-1726-2014,     UPC Futur 

LinkedIn,      ResearchGate

Journal Articles top

Book Chapters and Bookstop

L. Mora and Vilaplana, V., MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020, vol. 12658, Springer International Publishing, 2021, pp. 376-390.
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.

Conference Papers top

In Press
O. Pina and Vilaplana, V., Self-supervised graph representations of WSIs, in Geometric Deep Learning in Medical Image Analysis, In Press.
C. Hernandez, Vilaplana, V., Combalia, M., García, S., Podlipnik, S., Burgos, J., Puig, S., and Malvehy, J., Sentinel lymph node status prediction with self-attention neural networks using histologies of primary melanoma tumours, in European Association of Dermato Oncology (EADO 2022), 2022.
M. Combalia, Podlipnik, S., Hernandez, C., García, S., Ficapal, J., Burgos, J., Vilaplana, V., and Malvehy, J., Artificial intelligence to predict positivity of sentinel lymph node biopsy in melanoma patients, in European Association of Dermato Oncology (EADO 2022), 2022.
I. Cumplido-Mayoral, Garcia-Prat, M., Operto, G., Falcon, C., Shekari, M., Cacciaglia, R., Mila-Aloma, M., Calvet, M. Suarez, Vilaplana, V., and Gispert, J. D., Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer’s Disease and Neurodegeneration, in Alzheimer's Association International Conference, 2022.
C. Hernandez, Combalia, M., Puig, S., Malvehy, J., and Vilaplana, V., Contrastive and attention-based multiple instance learning for the prediction of sentinel lymph node status from histopathologies of primary melanoma tumours., in Cancer Prevention through early detecTion (Caption) Workshop at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), 2022.

Research Areas top

Biomedical Applications Internal Jan
Region-based image and video processing Internal Jan
Deep learning Internal Jun
Saliency prediction Internal Feb
Multimedia Retrieval Internal Sep