> Details
Maron, Roman C.
[Author];
Weichenthal, Michael
[Author];
Utikal, Jochen
[Author];
Hekler, Achim
[Author];
Berking, Carola
[Author];
Hauschild, Axel
[Author];
Enk, Alexander
[Author];
Haferkamp, Sebastian
[Author];
Klode, Joachim
[Author];
Schadendorf, Dirk
[Author];
Jansen, Philipp
[Author];
Holland-Letz, Tim
[Author];
Schilling, Bastian
[Author];
Kalle, Christof von
[Author];
Fröhling, Stefan
[Author];
Gaiser, Maria
[Author];
Hartmann, Daniela
[Author];
Gesierich, Anja Heike
[Author];
Kähler, Katharina C.
[Author];
Wehkamp, Ulrike
[Author];
Karoglan, Ante
[Author];
Bär, Claudia
[Author];
Brinker, Titus Josef
[Author]
Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks
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- Media type: E-Article
- Title: Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks
- Contributor: Maron, Roman C. [VerfasserIn]; Weichenthal, Michael [VerfasserIn]; Utikal, Jochen [VerfasserIn]; Hekler, Achim [VerfasserIn]; Berking, Carola [VerfasserIn]; Hauschild, Axel [VerfasserIn]; Enk, Alexander [VerfasserIn]; Haferkamp, Sebastian [VerfasserIn]; Klode, Joachim [VerfasserIn]; Schadendorf, Dirk [VerfasserIn]; Jansen, Philipp [VerfasserIn]; Holland-Letz, Tim [VerfasserIn]; Schilling, Bastian [VerfasserIn]; Kalle, Christof von [VerfasserIn]; Fröhling, Stefan [VerfasserIn]; Gaiser, Maria [VerfasserIn]; Hartmann, Daniela [VerfasserIn]; Gesierich, Anja Heike [VerfasserIn]; Kähler, Katharina C. [VerfasserIn]; Wehkamp, Ulrike [VerfasserIn]; Karoglan, Ante [VerfasserIn]; Bär, Claudia [VerfasserIn]; Brinker, Titus Josef [VerfasserIn]
- Published: 14 August 2019
- Published in: European journal of cancer ; 119(2019), Seite 57-65
- Language: English
- DOI: 10.1016/j.ejca.2019.06.013
- ISSN: 1879-0852
- Keywords: Artificial intelligence ; Melanoma ; Skin cancer ; Skin cancer screening
- Description: Background - Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. - Methods - Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. - Findings - Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). - Interpretation - Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001).
- Footnote:
- Access State: Open Access