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

Medicine and Health

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

S. Kalra, H. R. Tizhoosh, et al.

This groundbreaking research conducted by Shivam Kalra, H. R. Tizhoosh, and their colleagues investigates how AI-powered image search can significantly enhance diagnostic accuracy in histopathology. With nearly 30,000 whole-slide images analyzed, their innovative majority voting approach demonstrates a path toward improved consensus in cancer subtype diagnosis.

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Playback language: English
Abstract
This study explores using AI-based image search to improve diagnostic accuracy in histopathology. By searching the TCGA database (nearly 30,000 whole-slide images), the researchers successfully indexed and searched 20 million image patches. Employing a conservative "majority voting" approach, they achieved high accuracy in identifying cancer subtypes from both frozen section and permanent slides. The findings suggest that computational consensus is achievable for diagnosis given sufficient searchable cases per cancer subtype.
Publisher
npj Digital Medicine
Published On
Mar 10, 2020
Authors
Shivam Kalra, H. R. Tizhoosh, Sultaan Shah, Charles Choi, Savvas Damaskinos, Amir Safarpoor, Sobhan Shafiei, Morteza Babaie, Phedias Diamandis, Clinton J. V. Campbell, Liron Pantanowitz
Tags
AI-based image search
diagnostic accuracy
histopathology
cancer subtypes
TCGA database
image indexing
majority voting

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