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.
Ianni, Julianna D.; Soans, Rajath E.; Sankarapandian, Sivaramakrishnan; Chamarthi, Ramachandra Vikas; Ayyagari, Devi; Olsen, Thomas G.; Bonham, Michael J.; Stavish, Coleman C.; Motaparthi, Kiran; Cockerell, Clay J.; Feeser, Theresa A.; and Lee, Jason B., "Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload." (2020). Department of Dermatology and Cutaneous Biology Faculty Papers. Paper 128.
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