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Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

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Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. / Brinker, Titus J; Hekler, Achim; Enk, Alexander H; Klode, Joachim; Hauschild, Axel; Berking, Carola; Schilling, Bastian; Haferkamp, Sebastian; Schadendorf, Dirk; Holland-Letz, Tim; Utikal, Jochen S; von Kalle, Christof; Collaborators.

in: EUR J CANCER, Jahrgang 113, 05.2019, S. 47-54.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Brinker, TJ, Hekler, A, Enk, AH, Klode, J, Hauschild, A, Berking, C, Schilling, B, Haferkamp, S, Schadendorf, D, Holland-Letz, T, Utikal, JS, von Kalle, C & Collaborators 2019, 'Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task', EUR J CANCER, Jg. 113, S. 47-54. https://doi.org/10.1016/j.ejca.2019.04.001

APA

Brinker, T. J., Hekler, A., Enk, A. H., Klode, J., Hauschild, A., Berking, C., Schilling, B., Haferkamp, S., Schadendorf, D., Holland-Letz, T., Utikal, J. S., von Kalle, C., & Collaborators (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. EUR J CANCER, 113, 47-54. https://doi.org/10.1016/j.ejca.2019.04.001

Vancouver

Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. EUR J CANCER. 2019 Mai;113:47-54. https://doi.org/10.1016/j.ejca.2019.04.001

Bibtex

@article{80be361c7cdd4c20b055a98bd4a9a288,
title = "Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task",
abstract = "BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy.METHODS: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics.FINDINGS: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%.INTERPRETATION: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity.",
author = "Brinker, {Titus J} and Achim Hekler and Enk, {Alexander H} and Joachim Klode and Axel Hauschild and Carola Berking and Bastian Schilling and Sebastian Haferkamp and Dirk Schadendorf and Tim Holland-Letz and Utikal, {Jochen S} and {von Kalle}, Christof and Collaborators and Christoffer Gebhardt and Nina Booken and Christolouka, {Maria Dimitra}",
note = "Copyright {\textcopyright} 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.",
year = "2019",
month = may,
doi = "10.1016/j.ejca.2019.04.001",
language = "English",
volume = "113",
pages = "47--54",
journal = "EUR J CANCER",
issn = "0959-8049",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

AU - Brinker, Titus J

AU - Hekler, Achim

AU - Enk, Alexander H

AU - Klode, Joachim

AU - Hauschild, Axel

AU - Berking, Carola

AU - Schilling, Bastian

AU - Haferkamp, Sebastian

AU - Schadendorf, Dirk

AU - Holland-Letz, Tim

AU - Utikal, Jochen S

AU - von Kalle, Christof

AU - Collaborators

AU - Gebhardt, Christoffer

AU - Booken, Nina

AU - Christolouka, Maria Dimitra

N1 - Copyright © 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.

PY - 2019/5

Y1 - 2019/5

N2 - BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy.METHODS: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics.FINDINGS: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%.INTERPRETATION: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity.

AB - BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy.METHODS: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics.FINDINGS: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%.INTERPRETATION: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity.

U2 - 10.1016/j.ejca.2019.04.001

DO - 10.1016/j.ejca.2019.04.001

M3 - SCORING: Journal article

C2 - 30981091

VL - 113

SP - 47

EP - 54

JO - EUR J CANCER

JF - EUR J CANCER

SN - 0959-8049

ER -