Improving Skin Cancer Classification Using Heavy-Tailed Student T-Distribution in Generative Adversarial Networks (TED-GAN)

Deep learning has gained immense attention from researchers in medicine, especially in medical imaging. The main bottleneck is the unavailability of sufficiently large medical datasets required for the good performance of deep learning models. This paper proposes a new framework consisting of one va...

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Detalles Bibliográficos
Autores principales:	Bilal Ahmad, Sun Jun, Vasile Palade, Qi You, Li Mao, Mao Zhongjie
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	variational autoencoder generative adversarial networks melanoma detection skin cancer classification student t-distribution heavy-tailed distribution Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/3b9db49dbab84d11853d3f1754a2ff87
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Improving Skin Cancer Classification Using Heavy-Tailed Student T-Distribution in Generative Adversarial Networks (TED-GAN)

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