Document Type
Article
Publication Date
2-21-2020
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.
Recommended Citation
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.
https://jdc.jefferson.edu/dcbfp/128
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
32081956
Language
English
Comments
This article is the author’s final published version in Scientific Reports, Volume 10, Issue 1, February 2020, Page 3217.
The published version is available at https://doi.org/10.1038/s41598-020-59985-2. Copyright © Ianni et al.