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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2022
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| Materias: | |
| 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 |