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

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 paradigm...

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Detalles Bibliográficos
Autores principales: Mascolini, Alessio, Cardamone, Dario, Ponzio, Francesco, Di Cataldo, Santa, Ficarra, Elisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308954/
https://www.ncbi.nlm.nih.gov/pubmed/35871688
http://dx.doi.org/10.1186/s12859-022-04845-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9308954/
https://www.ncbi.nlm.nih.gov/pubmed/35871688
http://dx.doi.org/10.1186/s12859-022-04845-1

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