Predicting risk of cardiovascular disease using retinal OCT imaging

no code implementations • 26 Mar 2024 • Cynthia Maldonado-Garcia, Rodrigo Bonazzola, Enzo Ferrante, Thomas H Julian, Panagiotis I Sergouniotis, Nishant Ravikumara, Alejandro F Frangi

A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate between patients at risk of CVD events (MI or stroke) and non-CVD cases.

feature selection

Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI

no code implementations • 22 Nov 2023 • Nicolás Gaggion, Benjamin A. Matheson, Yan Xia, Rodrigo Bonazzola, Nishant Ravikumar, Zeike A. Taylor, Diego H. Milone, Alejandro F. Frangi, Enzo Ferrante

Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function.

Anatomy

Learned Local Attention Maps for Synthesising Vessel Segmentations

no code implementations • 24 Aug 2023 • Yash Deo, Rodrigo Bonazzola, Haoran Dou, Yan Xia, Tianyou Wei, Nishant Ravikumar, Alejandro F. Frangi, Toni Lassila

We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI.

Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology

no code implementations • 7 Jan 2023 • Rodrigo Bonazzola, Enzo Ferrante, Nishant Ravikumar, Yan Xia, Bernard Keavney, Sven Plein, Tanveer Syeda-Mahmood, Alejandro F Frangi

Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE).