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

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.

Citation:

Mascolini, Alessio; Cardamone, Dario; Ponzio, Francesco; Di Cataldo, Santa; Ficarra, Elisa "Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations" BMC BIOINFORMATICS, vol. 23, pp. 295 -312 , 2022 DOI: 10.1186/s12859-022-04845-1

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• DOI: 10.1186/s12859-022-04845-1