The impact of site-specific digital histology signatures on deep learning model accuracy and bias
Frederick M. Howard; James M. Dolezal; Sara Kochanny; Jefree J. Schulte; Heather Chen; Lara R. Heij; Dezheng Huo; Rita Nanda · 2021 · Nature Communications
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
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Abstract
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. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normaliz
Abstract by Frederick M. Howard; James M. Dolezal; Sara Kochanny; Jefree J. Schulte; Heather Chen; Lara R. Heij; Dezheng Huo; Rita Nanda, Nature Communications (2021) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41467-021-24698-1