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

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.

23 pages, 5 figures

References in corpus (3)

• Automated Gleason Grading of Prostate Biopsies using Deep Learning
• An attention-based multi-resolution model for prostate whole slide imageclassification and localization
• Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence

Cited by in corpus (5)

• Artificial Intelligence for Digital and Computational Pathology
• Streaming convolutional neural networks for end-to-end learning with multi-megapixel images
• Recommendations on test datasets for evaluating AI solutions in pathology
• Autofocusing technologies for whole slide imaging and automated microscopy
• Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images -- Nevus & Melanoma