27 citations · 37 across the 7 of their papers we have counts for
7 papers
A Neuroimaging Simulation Framework for Developing and Evaluating Causal AI
Eryn Libert-Scott, Emma A. M. Stanley, Vibujithan Vigneshwaran +3
Causally linking disease-related factors to image-derived biomarkers provides a powerful pathway to understanding disease mechanisms. Despite growing interest in applying causal ar…
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…
MACAW: A Causal Generative Model for Medical Imaging
Vibujithan Vigneshwaran, Erik Ohara, Matthias Wilms +1
Although deep learning techniques show promising results for many neuroimaging tasks in research settings, they have not yet found widespread use in clinical scenarios. One of the…
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…
Bidirectional Modeling and Analysis of Brain Aging with Normalizing Flows
Matthias Wilms, Jordan J. Bannister, Pauline Mouches +4
Brain aging is a widely studied longitudinal process throughout which the brain undergoes considerable morphological changes and various machine learning approaches have been propo…
Population-based Respiratory 4D Motion Atlas Construction and its Application for VR Simulations of Liver Punctures
Andre Mastmeyer, Matthias Wilms, Heinz Handels
Virtual reality (VR) training simulators of liver needle insertion in the hepatic area of breathing virtual patients currently need 4D data acquisitions as a prerequisite. Here, fi…