Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.

Publication date: Jul 17, 2019

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 on 25-26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison.

A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p

Hekler, A., Utikal, J.S., Enk, A.H., Solass, W., Schmitt, M., Klode, J., Schadendorf, D., Sondermann, W., Franklin, C., Bestvater, F., Flaig, M.J., Krahl, D., von Kalle, C., Fr”ohling, S., and Brinker, T.J. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. 23433. 2019 Eur J Cancer (118):

Concepts Keywords
Benign Artificial intelligence
Board Certified Neural network
CNN Cybernetics
Convolutional Neural Network Articles
Deep Learning Artificial intelligence
Eosin Convolutional neural network
Haematoxylin Deep learning
Histopathologic Melanoma
Histopathological RTT
Histopathologist Cancer
Malignant Melanoma Artificial neural networks
Melanoma Convolutional neural network
Microscope
Nevi
Nevus
Pathologist
Tissue Biopsy

Semantics

Type Source Name
gene UNIPROT DNMT1
gene UNIPROT CD69
gene UNIPROT CD5L
disease MESH nevus
disease MESH cancers
disease MESH diagnosis
pathway BSID Melanoma
disease DOID melanoma
disease MESH melanoma

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