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arXiv · 1909.11212

Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World

Julianna D. Ianni·Rajath E. Soans·Sivaramakrishnan Sankarapandian·Ramachandra Vikas Chamarthi·Devi Ayyagari·Thomas G. Olsen·Michael J. Bonham·Coleman C. Stavish·Kiran Motaparthi·Clay J. Cockerell·Theresa A. Feeser·Jason B. Lee
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Abstract

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin \& eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system's use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98\%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78\%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications.

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Julianna D. Ianni, Rajath E. Soans, Sivaramakrishnan Sankarapandian, Ramachandra Vikas Chamarthi, Devi Ayyagari, Thomas G. Olsen, Michael J. Bonham, Coleman C. Stavish, Kiran Motaparthi, Clay J. Cockerell, Theresa A. Feeser, Jason B. Lee. 2019-09-24. Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World. https://doi.org/10.1038/s41598-020-59985-2

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