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

Robin Louiset, Edouard Duchesnay, Antoine Grigis, Benoit Dufumier, Pietro Gori

Keywords: Contrastive Analysis, Variational Auto-Encoders, Medical Imaging

TL;DR: This paper proposes a new VAE model that aims at separating the pathological patterns unique to a disease dataset from the patterns common with a healthy dataset.

Abstract:

Contrastive Analysis VAEs (CA-VAEs) are 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.
To do so, these methods separate the latent space into a set of salient features (i.e., proper to the target dataset) and a set of common features (i.e., exist in both datasets). Currently, all models fail to prevent the sharing of information between latent spaces effectively and to capture all salient factors of variation.
To this end, we introduce two crucial regularization losses: a disentangling term between common and salient representations and a classification term between background and target samples in the salient space. We show a better performance than previous CA-VAEs methods on three medical applications and a natural images dataset (CelebA). Code and datasets are available at https://github.com/neurospin-projects/2023_rlouiset_sepvae.

Submission Number: 46