Search Results for author:
Found 11 papers, 6 papers with code
Separating common from salient patterns with Contrastive Representation Learning
Then, we motivate a novel Mutual Information minimization strategy to prevent information leakage between common and salient distributions.
SepVAE: a contrastive VAE to separate pathological patterns from healthy ones
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., healthy subjects) and a target dataset (TG) (i. e., patients) from the ones that only exist in the target dataset.
Contrastive learning for regression in multi-site brain age prediction
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 find new biomarkers.
Integrating Prior Knowledge in Contrastive Learning with Kernel
To this end, we use kernel theory to propose a novel loss, called decoupled uniformity, that i) allows the integration of prior knowledge and ii) removes the negative-positive coupling in the original InfoNCE loss.
Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels
However, a particularity of medical images is the availability of meta-data (such as age or sex) that can be exploited for learning representations.
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning
Our method is generic, it can integrate any clustering method and can be driven by both binary classification and regression.
Contrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution.
Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning
Moreover, we showed that linear models perform comparably with SOTA CNN on VBM data.
Structured Sparse Principal Components Analysis with the TV-Elastic Net penalty
However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population.
Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging
Nesterov's smoothing technique can be used to minimize a large number of non-smooth convex structured penalties but reasonable precision requires a small smoothing parameter, which slows down the convergence speed.
Predictive support recovery with TV-Elastic Net penalty and logistic regression: an application to structural MRI
This algorithm uses Nesterov's smoothing technique to approximate the TV penalty with a smooth function such that the loss and the penalties are minimized with an exact accelerated proximal gradient algorithm.
