Article

Exploiting Generative Self-Supervised Learning For The Assessment of Biological Images With Lack of Annotations: A COVID-19 Case-Study

2021-08-12

Abstract excerpt

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 GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. We show that Wasserstein Generative Adversarial Netw...

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Literature Corpus work: fc596434-bf71-574e-a593-2a6fa1dca56f
DOI: 10.21203/rs.3.rs-757777/v1

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