Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images.

Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations

Dario Cardamone;Francesco Ponzio;
2022-01-01

Abstract

Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images.
2022
23
295
1
17
Self-supervised learning, Fluorescent biological images, Generative adversarial network
Alessio Mascolini , Dario Cardamone , Francesco Ponzio , Santa Di Cataldo , Elisa Ficarra
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1878276
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