1 code implementation • 12 Jul 2023 • Robin Louiset, Edouard Duchesnay, Antoine Grigis, Benoit Dufumier, Pietro Gori
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.
1 code implementation • 14 Nov 2022 • Carlo Alberto Barbano, Benoit Dufumier, Edouard Duchesnay, Marco Grangetto, Pietro Gori
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.
1 code implementation • 10 Nov 2022 • Carlo Alberto Barbano, Benoit Dufumier, Enzo Tartaglione, Marco Grangetto, Pietro Gori
In this work, we tackle the problem of learning representations that are robust to biases.
1 code implementation • 3 Jun 2022 • Benoit Dufumier, Carlo Alberto Barbano, Robin Louiset, Edouard Duchesnay, Pietro Gori
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.
no code implementations • 10 Nov 2021 • Benoit Dufumier, Pietro Gori, Julie Victor, Antoine Grigis, Edouard Duchesnay
However, a particularity of medical images is the availability of meta-data (such as age or sex) that can be exploited for learning representations.
1 code implementation • 5 Jul 2021 • Robin Louiset, Pietro Gori, Benoit Dufumier, Josselin Houenou, Antoine Grigis, Edouard Duchesnay
Our method is generic, it can integrate any clustering method and can be driven by both binary classification and regression.
2 code implementations • 16 Jun 2021 • Benoit Dufumier, Pietro Gori, Julie Victor, Antoine Grigis, Michel Wessa, Paolo Brambilla, Pauline Favre, Mircea Polosan, Colm McDonald, Camille Marie Piguet, Edouard Duchesnay
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution.
1 code implementation • 2 Jun 2021 • Benoit Dufumier, Pietro Gori, Ilaria Battaglia, Julie Victor, Antoine Grigis, Edouard Duchesnay
Moreover, we showed that linear models perform comparably with SOTA CNN on VBM data.