117 citations · 138 across the 15 of their papers we have counts for
15 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…
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…
What to align in multimodal contrastive learning?
Benoit Dufumier, Javiera Castillo-Navarro, Devis Tuia +1
Humans perceive the world through multisensory integration, blending the information of different modalities to adapt their behavior. Contrastive learning offers an appealing solut…
Anatomical Foundation Models for Brain MRIs
Carlo Alberto Barbano, Matteo Brunello, Benoit Dufumier +1
Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative disorders. One of the most predominant biomarkers in…
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.,…
Contrastive learning for regression in multi-site brain age prediction
Carlo Alberto Barbano, Benoit Dufumier, Edouard Duchesnay +2
Building accurate Deep Learning (DL) models for brain age prediction is a very relevant topic in neuroimaging, as it could help better understand neurodegenerative disorders and fi…
Unbiased Supervised Contrastive Learning
Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione +2
Many datasets are biased, namely they contain easy-to-learn features that are highly correlated with the target class only in the dataset but not in the true underlying distributio…
Integrating Prior Knowledge in Contrastive Learning with Kernel
Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset +2
Data augmentation is a crucial component in unsupervised contrastive learning (CL). It determines how positive samples are defined and, ultimately, the quality of the learned repre…
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…
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
Yang Chen, Ward B. Manchester, Alfred O. Hero +7
We present several methods towards construction of precursors, which show great promise towards early predictions, of solar flare events in this paper. A data pre-processing pipeli…