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 / Mascolini, Alessio; Cardamone, Dario; Ponzio, Francesco; Di Cataldo, Santa; Ficarra, Elisa. - In: BMC BIOINFORMATICS. - ISSN 1471-2105. - 23:1(2022), pp. 295-312. [10.1186/s12859-022-04845-1]

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

Ficarra, Elisa
Supervision
2022

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
24-lug-2022
23
1
295
312
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations / Mascolini, Alessio; Cardamone, Dario; Ponzio, Francesco; Di Cataldo, Santa; Ficarra, Elisa. - In: BMC BIOINFORMATICS. - ISSN 1471-2105. - 23:1(2022), pp. 295-312. [10.1186/s12859-022-04845-1]
Mascolini, Alessio; Cardamone, Dario; Ponzio, Francesco; Di Cataldo, Santa; Ficarra, Elisa
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1284304
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