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

Shivam Kalra^1,2, H R Tizhoosh^2,3, Sultaan Shah¹, Charles Choi¹, Savvas Damaskinos¹, Amir Safarpoor², Sobhan Shafiei², Morteza Babaie², Phedias Diamandis⁴, Clinton J V Campbell^5,6, Liron Pantanowitz⁷

¹Huron Digital Pathology, St. Jacobs, ON Canada.

²2Kimia Lab, University of Waterloo, Waterloo, ON Canada.

³3Vector Institute, MaRS Centre, Toronto, ON Canada.

⁴4General Hospital/Research Institute (UHN), Toronto, Canada.

⁵5Stem Cell and Cancer Research Institute, McMaster University, Hamilton, Canada.

⁶6Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada.

⁷7Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA USA.

PubMed PMC DOI PDF

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 patholog…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.