output
20162025
most citedHandling confounding variables in statistical shape analysis -- application to cardiac remodelling

14 citations

researchers with a paper here
collaborating institutions

4 papers

eess.IV2025

Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomics Features from Multimodal Retinal Images

Ariadna Tohà-Dalmau, Josep Rosinés-Fonoll, Enrique Romero +11

This study aimed to develop a machine learning (ML) algorithm capable of determining cardiovascular risk in multimodal retinal images from patients with type 1 diabetes mellitus, d…

cs.CV2021

XDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System In Colorectal Cancer

Aurelia Bustos, Artemio Payá, Andres Torrubia +5

We present a system for the prediction of microsatellite instability (MSI) from H&E images of colorectal cancer using deep learning (DL) techniques customized for tissue microarray…

cs.CV2020★ 14 cited

Handling confounding variables in statistical shape analysis -- application to cardiac remodelling

Gabriel Bernardino, Oualid Benkarim, María Sanz-de la Garza +7

Statistical shape analysis is a powerful tool to assess organ morphologies and find shape changes associated to a particular disease. However, imbalance in confounding factors, suc…

cs.CV2016★ 5 cited

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

David Vázquez, Jorge Bernal, F. Javier Sánchez +5

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for…