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pro vyhledávání: '"Louiset, P."'
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 (CA) deals with the discovery of what is common and what is distinctive of a target domain compared to a background one. This is of great interest in many applications, such as medical imaging. Current state-of-the-art (SOTA) met
Externí odkaz:
http://arxiv.org/abs/2401.17776
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
Autor:
Benoit Dufumier, Pietro Gori, Sara Petiton, Robin Louiset, Jean-François Mangin, Antoine Grigis, Edouard Duchesnay
Publikováno v:
NeuroImage, Vol 296, Iss , Pp 120665- (2024)
The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medi
Externí odkaz:
https://doaj.org/article/6185c233d77248ac97b35bdc87d5448c
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:
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
Akademický článek
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Akademický článek
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Autor:
Laura Moutard, Caroline Goudin, Catherine Jaeger, Céline Duparc, Estelle Louiset, Tony Pereira, François Fraissinet, Marion Delessard, Justine Saulnier, Aurélie Rives-Feraille, Christelle Delalande, Hervé Lefebvre, Nathalie Rives, Ludovic Dumont, Christine Rondanino
Publikováno v:
eLife, Vol 12 (2023)
Children undergoing cancer treatments are at risk for impaired fertility. Cryopreserved prepubertal testicular biopsies could theoretically be later matured in vitro to produce spermatozoa for assisted reproductive technology. A complete in vitro spe
Externí odkaz:
https://doaj.org/article/7708908cd703458cb420d7a9aeb4be9b
Autor:
Sonja Schönecker, Verena Hoffmann, Fady Albashiti, Reinhard Thasler, Marlien Hagedorn, Marie-Luise Louiset, Anna Kopczak, Jennifer Rösler, Enayatullah Baki, Silke Wunderlich, Florian Kohlmayer, Klaus Kuhn, Martin Boeker, Johannes Tünnerhoff, Sven Poli, Ulf Ziemann, Oliver Kohlbacher, Katharina Althaus, Susanne Müller, Albert Ludolph, Hans A. Kestler, Ulrich Mansmann, Marianne Dieterich, Lars Kellert
Publikováno v:
BMC Neurology, Vol 23, Iss 1, Pp 1-7 (2023)
Abstract Background Although of high individual and socioeconomic relevance, a reliable prediction model for the prognosis of juvenile stroke (18–55 years) is missing. Therefore, the study presented in this protocol aims to prospectively validate t
Externí odkaz:
https://doaj.org/article/ea932bc9413c4c2ea8ce878fdcae66ce