Home Publications Research Grants Inventions & Patents Awards Additional Research Activities Faculties & Institutions Research Areas

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Maron RC, Weichenthal M, Utikal JS, Hekler A, Berking C, Hauschild A, Enk AH, Haferkamp S, Klode J, Schadendorf D, Jansen P, Holland-Letz T, Schilling B, Von Kalle C, Froehling S, Gaiser MR, Hartmann D, Gesierich A, Kaehler KC, Wehkamp U, Karoglan A, Baer C, Brinker TJ, Schmitt L, Peitsch WK, Hoffmann F, Becker JC, Drusio C, Jansen P, Klode J, Lodde G, Sammet S, Schadendorf D, Sondermann W, Ugurel S, Zader J, Enk A, Salzmann M, Schaefer S, Schaekel K, Winkler J, Woelbing P, Asper H, Bohne AS, Brown V, Burba B, Deffaa S, Dietrich C, Dietrich M, Drerup KA, Egberts F, Erkens AS, Greven S, Harde V, Jost M, Kaeding M, Kosova K, Lischner S, Maagk M, Messinger AL, Metzner M, Motamedi R, Rosenthal AC, Seidl U, Stemmermann J, Torz K, Velez JG, Haiduk J, Alter M, Baer C, Bergenthal P, Gerlach A, Holtorf C, Karoglan A, Kindermann S, Kraas L, Felcht M, Gaiser MR, Klemke CD, Kurzen H, Leibing T, Mueller V, Reinhard RR, Utikal J, Winter F, Berking C, Eicher L, Hartmann D, Heppt M, Kilian K, Krammer S, Lill D, Niesert AC, Oppel E, Sattler E, Senner S, Wallmichrath J, Wolff H, Giner T, Glutsch V, Kerstan A, Presser D, Schruefer P, Schummer P, Stolze I, Weber J, Drexler K, Haferkamp S, Mickler M, Stauner CT, Thiem A (2019)

---

Publication Type: Journal article

Publication year: 2019

Journal

Book Volume: 119

Pages Range: 57-65

DOI: 10.1016/j.ejca.2019.06.013

Abstract

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).

Authors with CRIS profile

Involved external institutions

How to cite

APA:

Maron, R.C., Weichenthal, M., Utikal, J.S., Hekler, A., Berking, C., Hauschild, A.,... Thiem, A. (2019). Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks. European Journal of Cancer, 119, 57-65. https://doi.org/10.1016/j.ejca.2019.06.013

MLA:

Maron, Roman C., et al. "Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks." European Journal of Cancer 119 (2019): 57-65.

BibTeX: Download