Elsevier

European Journal of Cancer

Volume 115, July 2019, Pages 79-83
European Journal of Cancer

Original Research
Pathologist-level classification of histopathological melanoma images with deep neural networks

https://doi.org/10.1016/j.ejca.2019.04.021Get rights and content
Under a Creative Commons license
open access

Highlights

  • A convolutional neural network (CNN) was trained with 595 histopathologic images of melanomas and nevi that were classified by an expert dermatohistopathologist.

  • The CNN was then tested with 100 additional images (melanoma/nevi = 1:1) and revealed a discordance of only 19% to the histopathologist.

  • Thus, even in the worst case, the discordance of the CNN is about the same compared with the discordance between human pathologists as reported in the literature (25–26%).

Abstract

Background

The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25–26% of discordance for classifying a benign nevus versus malignant melanoma. Deep learning was successfully implemented to enhance the precision of lung and breast cancer diagnoses. The aim of this study is to illustrate the potential of deep learning to assist human assessment for a histopathologic melanoma diagnosis.

Methods

Six hundred ninety-five lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi and 345 melanomas). Only the haematoxylin and eosin stained (H&E) slides of these lesions were digitalised using a slide scanner and then randomly cropped. Five hundred ninety-five of the resulting images were used for the training of a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison with the original class labels.

Findings

The total discordance with the histopathologist was 18% for melanoma (95% confidence interval [CI]: 7.4–28.6%), 20% for nevi (95% CI: 8.9–31.1%) and 19% for the full set of images (95% CI: 11.3–26.7%).

Interpretation

Even in the worst case, the discordance of the CNN was about the same compared with the discordance between human pathologists as reported in the literature. Despite the vastly reduced amount of data, time necessary for diagnosis and cost compared with the pathologist, our CNN archived on-par performance. Conclusively, CNNs indicate to be a valuable tool to assist human melanoma diagnoses.

Keywords

Melanoma
Pathology
Histopathology
Deep learning
Artificial intelligence

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