Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images.

ABSTRACT: Because histologic types are subjective and difficult to reproduce between pathologists, tissue morphology often takes a back seat to molecular testing for the selection of breast cancer treatments. This work explores whether a deep-learning algorithm can learn objective histologic H&E features that predict the clinical subtypes of breast cancer, as assessed by immunostaining for estrogen, progesterone, and Her2 receptors (ER/PR/Her2). Translating deep learning to this and related problems in histopathology presents a challenge due to the lack of large, well-annotated data sets, which are typically required for the algorithms to learn statistically significant discriminatory patterns. To overcome this limitation, we introduce the concept of "tissue fingerprints," which leverages large, una

SUBMITTER: Rawat RR 

PROVIDER: S-EPMC7190637 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature