Edouard Duchesnay

Imaging Genetics: Bio-Informatics and Bio-Statistics Challenges

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

Irregular adaptative pyramid of agents for segmentation to interpretation of image

Voxelwise Multivariate Statistics and Brain-Wide Machine Learning Using the Full Diffusion Tensor

Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification

Does Connectome Harmonic Analysis Pass the Spin Test?

MixUp Brain-Cortical Augmentations in Self-supervised Learning

Integrating Prior Knowledge in Contrastive Learning with Kernel

Structured Multivariate Pattern Classification to Detect MRI Markers for an Early Diagnosis of Alzheimer's Disease

Fairness, Debiasing and Privacy in Computer Vision and Medical Imaging

Automatic Discovery of Disease Subgroups by Contrasting with Healthy Controls

In biomedical Subgroup Discovery, practitioners are interested in discovering interpretable and homogeneous subgroups within a group of patients. In this paper, assuming that healthy subjects...

A Unified Geometric Framework for Weighted Contrastive Learning

Contrastive learning (CL) aims to preserve relational structure between samples by learning representations that reflect a similarity graph. Yet, the geometry of the resulting embeddings remains...

Improving clinical interpretability of linear neuroimaging models through feature whitening

Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learned weights remains challenging, as they do not...

Robust brain age estimation from structural MRI with contrastive learning

Estimating brain age from structural MRI has emerged as a powerful tool for characterizing normative and pathological aging. In this work, we explore contrastive learning as a scalable and robust...

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...

Separating common from salient patterns with Contrastive Representation Learning

Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a target (i.e.,...

Integrating Prior Knowledge in Contrastive Learning with Kernel

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 representation. In...