Background: Machine learning (ML) methods are becoming more feasible for use in clinical and epidemiologic research of breast cancer, particularly when characterizing histopathology. Compared to supervised ML methods, unsupervised approaches… Click to show full abstract
Background: Machine learning (ML) methods are becoming more feasible for use in clinical and epidemiologic research of breast cancer, particularly when characterizing histopathology. Compared to supervised ML methods, unsupervised approaches represent an opportunity to distinguish features heretofore unknown. The purpose of this study was to use unsupervised deep learning methods to identify histopathological features in diagnostic breast cancer hematoxylin and eosin (HE 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2417.
Journal Title: Epidemiology
Year Published: 2019
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