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The impact of site-specific digital histology signatures on deep learning model accuracy and bias
The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these...
| Autores principales: | , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292530/ https://www.ncbi.nlm.nih.gov/pubmed/34285218 http://dx.doi.org/10.1038/s41467-021-24698-1 |