Search Results for author:
Found 7 papers, 5 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.
Can we Agree? On the Rashōmon Effect and the Reliability of Post-Hoc Explainable AI
Further work is needed with more model types, data domains, and explanation methods.
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.
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.
