Pan-cancer diagnostic consensus through searching archival histopathology images using artificial intelligence.

ABSTRACT: The emergence of digital pathology has opened new horizons for histopathology. Artificial intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with different tasks. Whereas AI-involving classification and segmentation methods have obvious benefits for image analysis, image search represents a fundamental shift in computational pathology. Matching the pathology of new patients with already diagnosed and curated cases offers pathologists a new approach to improve diagnostic accuracy through visual inspection of similar cases and computational majority vote for consensus building. In this study, we report the results from searching the largest public repository (The Cancer Genome Atlas, TCGA) of whole-slide images from almost 11,000 patients. We successf

SUBMITTER: Kalra S 

PROVIDER: S-EPMC7064517 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature