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

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

Abstract Motivation 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...

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Bibliographic Details
Main Authors: Alessio Mascolini, Dario Cardamone, Francesco Ponzio, Santa Di Cataldo, Elisa Ficarra
Format: Article
Language:English
Published: BMC 2022-07-01
Series:BMC Bioinformatics
Subjects:
Self-supervised learning
Fluorescent biological images
Generative adversarial network
Online Access:https://doi.org/10.1186/s12859-022-04845-1

Internet

https://doi.org/10.1186/s12859-022-04845-1

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