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

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...

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Veröffentlicht in:	Scientific reports 2020-04, Vol.10 (1), p.7275-7275, Article 7275
Hauptverfasser:	Rawat, Rishi R., Ortega, Itzel, Roy, Preeyam, Sha, Fei, Shibata, Darryl, Ruderman, Daniel, Agus, David B.
Format:	Artikel
Sprache:	eng
Schlagworte:	631/114/1305 631/114/1564 631/67/2321 692/4028/67/1347 Adult Aged Algorithms Biomarkers, Tumor - metabolism Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - metabolism Breast Neoplasms - pathology Deep Learning ErbB-2 protein Estrogen receptors Estrogens Female Go/no-go discrimination learning Histopathology Humanities and Social Sciences Humans Image Processing, Computer-Assisted Learning algorithms Machine learning Middle Aged multidisciplinary Progesterone Progesterone - metabolism Receptor, ErbB-2 - metabolism Receptors, Estrogen - metabolism Science Science (multidisciplinary) Statistical analysis Tissue Array Analysis
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