VAESim: A probabilistic approach for self-supervised prototype discovery

In medical image datasets, discrete labels are often used to describe a continuous spectrum of conditions, making unsupervised image stratification a challenging task. In this work, we propose VAESim, an architecture for image stratification based on a conditional variational autoencoder. VAESim lea...

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Vydané v:	Image and vision computing Ročník 137; s. 104746
Hlavní autori:	Ferrante, Matteo, Boccato, Tommaso, Spasov, Simeon, Duggento, Andrea, Toschi, Nicola
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.09.2023
Predmet:	Deep clustering Medical imaging Prototypes discovery Variational autoencoders Variational autoencoders Medical imaging Prototypes discovery Deep clustering
ISSN:	0262-8856
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