2 citations · 5 across the 10 of their papers we have counts for
10 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…
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…
Separating common from salient patterns with Contrastive Representation Learning
Robin Louiset, Edouard Duchesnay, Antoine Grigis +1
Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a…
Can we Agree? On the Rashōmon Effect and the Reliability of Post-Hoc Explainable AI
Clement Poiret, Antoine Grigis, Justin Thomas +1
The Rashōmon effect poses challenges for deriving reliable knowledge from machine learning models. This study examined the influence of sample size on explanations from models in a…
SepVAE: a contrastive VAE to separate pathological patterns from healthy ones
Robin Louiset, Edouard Duchesnay, Antoine Grigis +2
Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that aims at separating the common factors of variation between a background dataset (BG) (i.e.,…
Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels
Benoit Dufumier, Pietro Gori, Julie Victor +2
Contrastive Learning has shown impressive results on natural and medical images, without requiring annotated data. However, a particularity of medical images is the availability of…
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning
Robin Louiset, Pietro Gori, Benoit Dufumier +3
Subtype Discovery consists in finding interpretable and consistent sub-parts of a dataset, which are also relevant to a certain supervised task. From a mathematical point of view,…
Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification
Benoit Dufumier, Pietro Gori, Julie Victor +8
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution. For 3D medical images, it is often impractic…
Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning
Benoit Dufumier, Pietro Gori, Ilaria Battaglia +3
Deep Learning (DL) and specifically CNN models have become a de facto method for a wide range of vision tasks, outperforming traditional machine learning (ML) methods. Consequently…