3 citations · 4 across the 4 of their papers we have counts for
5 papers
Diffusion-Based Synthesis of Complete 3D Biparametric Prostate MRI
Claudia Giardina, Montse Pardàs, Verónica Vilaplana
Synthetic biparametric MRI (bpMRI) may help alleviate the limited availability of large and representative prostate MRI datasets for deep learning applications. In this work, we pr…
Cell Nuclei Detection and Classification in Whole Slide Images with Transformers
Oscar Pina, Eduard Dorca, Verónica Vilaplana
Accurate and efficient cell nuclei detection and classification in histopathological Whole Slide Images (WSIs) are pivotal for digital pathology applications. Traditional cell segm…
Layer-wise training for self-supervised learning on graphs
Oscar Pina, Verónica Vilaplana
End-to-end training of graph neural networks (GNN) on large graphs presents several memory and computational challenges, and limits the application to shallow architectures as dept…
SurvLIMEpy: A Python package implementing SurvLIME
Cristian Pachón-García, Carlos Hernández-Pérez, Pedro Delicado +1
In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learni…
Feature propagation as self-supervision signals on graphs
Oscar Pina, Verónica Vilaplana
Self-supervised learning is gaining considerable attention as a solution to avoid the requirement of extensive annotations in representation learning on graphs. Current algorithms…