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

Source: [Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World](<https://www.sota2.com/research/paper/68fa5f001e209131ce9e6d17>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:44:10.915Z

Authors: 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

Published: 2019-09-24T22:26:44.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1909.11212>)

[PDF](<https://arxiv.org/pdf/1909.11212.pdf>)