Article
The impact of site-specific digital histology signatures on deep learning model accuracy and bias.
Nature communications - 20 Jul 2021
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
Abstract excerpt
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 features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes....
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