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...

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Detalles Bibliográficos
Autores principales: Howard, Frederick M., Dolezal, James, Kochanny, Sara, Schulte, Jefree, Chen, Heather, Heij, Lara, Huo, Dezheng, Nanda, Rita, Olopade, Olufunmilayo I., Kather, Jakob N., Cipriani, Nicole, Grossman, Robert L., Pearson, Alexander T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
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

Internet

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

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