6 papers · 1 filter
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
From Redaction to Restoration: Deep Learning for Medical Image Anonymization and Reconstruction
Adrienne Kline, Abhijit Gaonkar, Daniel Pittman +2
Removing patient-specific information from medical images is crucial to enable sharing and open science without compromising patient identities. However, many methods currently use…
Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge
Rachid Zeghlache, Ikram Brahim, Pierre-Henri Conze +47
The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical cohe…
Semi-disentangled spatiotemporal implicit neural representations of longitudinal neuroimaging data for trajectory classification
Agampreet Aulakh, Nils D. Forkert, Matthias Wilms
The human brain undergoes dynamic, potentially pathology-driven, structural changes throughout a lifespan. Longitudinal Magnetic Resonance Imaging (MRI) and other neuroimaging data…
Exploring the interplay of label bias with subgroup size and separability: A case study in mammographic density classification
Emma A. M. Stanley, Raghav Mehta, Mélanie Roschewitz +2
Systematic mislabelling affecting specific subgroups (i.e., label bias) in medical imaging datasets represents an understudied issue concerning the fairness of medical AI systems.…
Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging
Emma A. M. Stanley, Raissa Souza, Anthony Winder +4
Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of disparities in performance between subgroups. Since not all so…