Search Results for author:
Found 8 papers, 6 papers with code
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.
Unbiased Supervised Contrastive Learning
In this work, we tackle the problem of learning representations that are robust to biases.
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.
