63.6k citations · 63.6k across the 10 of their papers we have counts for
11 papers
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…
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…
Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty
Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem +4
Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover dominant patterns of variability within a population. Despite its ability to repre…
Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging
Fouad Hadj-Selem, Tommy Lofstedt, Elvis Dohmatob +4
Predictive models can be used on high-dimensional brain images for diagnosis of a clinical condition. Spatial regularization through structured sparsity offers new perspectives in…
Predictive support recovery with TV-Elastic Net penalty and logistic regression: an application to structural MRI
Mathieu Dubois, Fouad Hadj-Selem, Tommy Lofstedt +4
The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relat…
Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort +16
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses…