Antoine Grigis

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. For 3D medical images, it is often impractical to build a...

Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be...

Can we Agree? On the Rash\=omon Effect and the Reliability of Post-Hoc Explainable AI

The Rash\=omon effect poses challenges for deriving reliable knowledge from machine learning models. This study examined the influence of sample size on explanations from models in a Rash\=omon...

Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy Labels

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 meta-data (such...

UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised Learning

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, this can be...

A new high order tensor decomposition: Application to reorientation

Structural-Connectivity-Based Individual Parcellations Improve Regional Cortical Thickness Heritability Study

How and Why Does Deep Ensemble Coupled with Transfer Learning Increase Performance in Bipolar Disorder and Schizophrenia Classification?

Exploration of the Neural Correlates of Consciousness Using Linear Latent Model

Heritability of surface area and cortical thickness: a comparison between the Human Connectome Project and the UK Biobank dataset

Supervised Diagnosis Prediction from Cortical Sulci: Toward the Discovery of Neurodevelopmental Biomarkers in Mental Disorders

Multi-View Variational Autoencoders Allow for Interpretability Leveraging Digital Avatars: Application to the HBN Cohort

Separating common from salient patterns with Contrastive Representation Learning

Interpretable automatic detection of incomplete hippocampal inversions using anatomical criteria

Analysis vs Synthesis-based Regularization for Combined Compressed Sensing and Parallel MRI Reconstruction at 7 Tesla

SepVAE: a contrastive VAE to separate pathological patterns from healthy ones

UCSL : A Machine Learning Expectation-Maximization Framework for Unsupervised Clustering Driven by Supervised Learning

Comparison of interpolation methods for angular resampling of diffusion weighted images