Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images - 13/01/18
for the
International Skin Imaging Collaboration
Abstract |
Background |
Computer vision may aid in melanoma detection.
Objective |
We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images.
Methods |
We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into “fusion” algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant.
Results |
The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001).
Limitations |
The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice.
Conclusion |
Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists.
Le texte complet de cet article est disponible en PDF.Key words : computer algorithm, computer vision, dermatologist, International Skin Imaging Collaboration, International Symposium on Biomedical Imaging, machine learning, melanoma, reader study, skin cancer
Abbreviations used : ISBI, ISIC, ROC, SVM
Plan
| Drs Marchetti and Codella contributed equally to this article. |
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| Supported in part by the National Institutes of Health/National Cancer Institute Cancer Center Support Grant P30 CA008748. |
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| Disclosure: Dr Codella is an employee of IBM and an IBM stockholder. Dr Scope is a consultant to Emerald Inc. Drs Marchetti, Dusza, Halpern, Marghoob, DeFazio, Yélamos, Carrera, Jaimes, Mishra, Kalloo, Quigley, Gutman, Helba, and Celebi have no conflicts of interest to declare. |
Vol 78 - N° 2
P. 270 - février 2018 Retour au numéro
Article précédent
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