5 papers
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…
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…
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,…
How and why does deep ensemble coupled with transfer learning increase performance in bipolar disorder and schizophrenia classification?
Sara Petiton, Antoine Grigis, Benoit Dufumier +1
Transfer learning (TL) and deep ensemble learning (DE) have recently been shown to outperform simple machine learning in classifying psychiatric disorders. However, there is still…
Robust brain age estimation from structural MRI with contrastive learning
Carlo Alberto Barbano, Benoit Dufumier, Edouard Duchesnay +2
Estimating brain age from structural MRI has emerged as a powerful tool for characterizing normative and pathological aging. In this work, we explore contrastive learning as a scal…