Veronica Vilaplana

Associate Professor

Technical University of Catalonia

Spain

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Dr. Veronica Vilaplana is an Associate Professor at the Technical University of Catalonia, Spain. Her research interests encompass areas such as survival models for melanoma prognosis, molecular subtyping in breast cancer, and neuroimaging data analysis related to brain aging trajectories. She has contributed to the development of advanced methodologies in machine learning, particularly in the application of self-supervised learning and unsupervised domain adaptation for medical imaging.

Articles (18)

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2025

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2020

Collaborators (3)

Javier Marcello

Universidad de Las Palmas de Gran Canaria

SPAIN

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Josep Ramon Morros Rubió

Associate Professor

Universitat Politècnica de Catalunya

SPAIN

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Eduard Gregorio López

Associate professor (Professor agregat/Contratado doctor)

Universitat de Girona

SPAIN

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Veronica Vilaplana

Research Interests

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About

Articles (18)

Comparative analysis and interpretability of survival models for melanoma prognosis

Breast Cancer Molecular Subtyping from H&E Whole Slide Images Using Foundation Models and Transformers

Identifying Brain Ageing Trajectories Using Variational Autoencoders with Regression Model in Neuroimaging Data Stratified by Sex and Validated Against Dementia-Related Risk Factors

Layer-Wise Training of Graph Neural Networks With Self-Supervised Learning

AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation

Feature propagation as self-supervision signals on graphs

Bridging Domains in Melanoma Diagnostics: Predicting BRAF Mutations and Sentinel Lymph Node Positivity with Attention-Based Models in Histological Images

Unsupervised Domain Adaptation for Multi-Stain Cell Detection in Breast Cancer with Transformers

Simultaneous fruit detection and size estimation using multitask deep neural networks

SurvLIMEpy: A Python package implementing SurvLIME

SEG-ESRGAN: A Multi-Task Network for Super-Resolution and Semantic Segmentation of Remote Sensing Images

Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer's Disease and Neurodegeneration stratified by sex

Contrastive and Attention-Based Multiple Instance Learning for the Prediction of Sentinel Lymph Node Status from Histopathologies of Primary Melanoma Tumours

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation-Analysis of Ranking Scores and Benchmarking Results

Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks

A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery

MRI Brain Tumor Segmentation and Uncertainty Estimation Using 3D-UNet Architectures

Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks

Collaborators (3)

Javier Marcello

Josep Ramon Morros Rubió

Eduard Gregorio López

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