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cs.LG2026
Automatic Discovery of Disease Subgroups by Contrasting with Healthy Controls
Robin Louiset, Edouard Duchesnay, Benoit Dufumier +2
In biomedical Subgroup Discovery, practitioners are interested in discovering interpretable and homogeneous subgroups within a group of patients. In this paper, assuming that healt…
cs.LG2026
A Unified Geometric Framework for Weighted Contrastive Learning
Raphael Vock, Edouard Duchesnay, Benoit Dufumier
Contrastive learning (CL) aims to preserve relational structure between samples by learning representations that reflect a similarity graph. Yet, the geometry of the resulting embe…
cs.LG2026
Improving clinical interpretability of linear neuroimaging models through feature whitening
Sara Petiton, Antoine Grigis, Raphaël Vock +1
Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learned weights remains challenging,…