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pro vyhledávání: '"Duchesnay, Edouard"'
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., diseased subjects), from the salient factors of variati
Externí odkaz:
http://arxiv.org/abs/2402.11928
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
Externí odkaz:
http://arxiv.org/abs/2307.06206
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. To estimate accurate and generalizable models, larg
Externí odkaz:
http://arxiv.org/abs/2211.08326
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 this work, we open the door to new perspectives for CL b
Externí odkaz:
http://arxiv.org/abs/2206.01646
Autor:
Dufumier, Benoit, Gori, Pietro, Petiton, Sara, Louiset, Robin, Mangin, Jean-François, Grigis, Antoine, Duchesnay, Edouard
Publikováno v:
In NeuroImage 1 August 2024 296
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 as age or sex) that can be exploited for learning rep
Externí odkaz:
http://arxiv.org/abs/2111.05643
Autor:
Louiset, Robin, Gori, Pietro, Dufumier, Benoit, Houenou, Josselin, Grigis, Antoine, Duchesnay, Edouard
Publikováno v:
ECML/PKDD 2021
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 defined as a clustering task driven by supervised learning
Externí odkaz:
http://arxiv.org/abs/2107.01988
Autor:
Dufumier, Benoit, Gori, Pietro, Victor, Julie, Grigis, Antoine, Wessa, Michel, Brambilla, Paolo, Favre, Pauline, Polosan, Mircea, McDonald, Colm, Piguet, Camille Marie, Duchesnay, Edouard
Publikováno v:
MICCAI 2021
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 large homogeneous annotated dataset for a specific patho
Externí odkaz:
http://arxiv.org/abs/2106.08808
Autor:
Dufumier, Benoit, Gori, Pietro, Battaglia, Ilaria, Victor, Julie, Grigis, Antoine, Duchesnay, Edouard
Deep Learning (DL) and specifically CNN models have become a de facto method for a wide range of vision tasks, outperforming traditional machine learning (ML) methods. Consequently, they drew a lot of attention in the neuroimaging field in particular
Externí odkaz:
http://arxiv.org/abs/2106.01132
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