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

التفاصيل البيبلوغرافية
العنوان: Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World
المؤلفون: 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., Lee, Jason B.
المصدر: Sci Rep 10, 3217 (2020)
سنة النشر: 2019
المجموعة: Computer Science
Quantitative Biology
مصطلحات موضوعية: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods, Quantitative Biology - Tissues and Organs
الوصف: 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.
Comment: 23 pages, 5 figures
نوع الوثيقة: Working Paper
DOI: 10.1038/s41598-020-59985-2
URL الوصول: http://arxiv.org/abs/1909.11212
رقم الأكسشن: edsarx.1909.11212
قاعدة البيانات: arXiv
الوصف
DOI:10.1038/s41598-020-59985-2