Man against Machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

Annals of Oncology

Volumn 29, Issue 8, 2018, Pages 1836-1842

  Haenssle, H A ^a   Fink, C ^a   Schneiderbauer, R ^a   Toberer, F ^a   Buhl, T ^b   Blum, A ^c   Kalloo, A ^d   Ben Hadj Hassen, A ^e   Thomas, L ^f   Enk, A ^a   Uhlmann, L ^a   Alt, Christina ^g   Arenbergerova, Monika ^g   Bakos, Renato ^g   Baltzer, Anne ^g   Bertlich, Ines ^g   Blum, Andreas ^g   Bokor Billmann, Therezia ^g   Bowling, Jonathan ^g   Braghiroli, Naira ^g   Braun, Ralph ^g   Buder Bakhaya, Kristina ^g   Buhl, Timo ^g   Cabo, Horacio ^g   Cabrijan, Leo ^g   Cevic, Naciye ^g   Classen, Anna ^g   Deltgen, David ^g   Fink, Christine ^g   Georgieva, Ivelina ^g   Hakim Meibodi, Lara Elena ^g   Hanner, Susanne ^g   Hartmann, Franziska ^g   Hartmann, Julia ^g   Haus, Georg ^g   Hoxha, Elti ^g   Karls, Raimonds ^g   Koga, Hiroshi ^g   Kreusch, Ju¨rgen ^g   Lallas, Aimilios ^g   Majenka, Pawel ^g   Marghoob, Ash ^g   Massone, Cesare ^g   Mekokishvili, Lali ^g   Mestel, Dominik ^g   Meyer, Volker ^g   Neuberger, Anna ^g   Nielsen, Kari ^g   Oliviero, Margaret ^g   Pampena, Riccardo ^g   Paoli, John ^g   Pawlik, Erika ^g   Rao, Barbar ^g   Rendon, Adriana ^g   Russo, Teresa ^g   Sadek, Ahmed ^g   Samhaber, Kinga ^g   Schneiderbauer, Roland ^g   Schweizer, Anissa ^g   Toberer, Ferdinand ^g   Trennheuser, Lukas ^g   Vlahova, Lyobomira ^g   Wald, Alexander ^g   Winkler, Julia ^g   Wo¨lbing, Priscila ^g   Zalaudek, Iris ^g   more..

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b UNIVERSITY OF GÖTTINGEN   (Germany)

^c Office Based Clinic of Dermatology   (Germany)

^d MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^e UNIVERSITY OF PASSAU   (Germany)

^f UNIVERSITÉ LYON 1   (France)

^g NONE

Author keywords

Automated melanoma detection; Computer algorithm; Deep learning convolutional neural network; Dermoscopy; Melanocytic nevi; Melanoma

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER CLASSIFICATION; CANCER DIAGNOSIS; COMPARATIVE STUDY; CROSS-SECTIONAL STUDY; DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK; DERMATOLOGIST; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; EPILUMINESCENCE MICROSCOPY; HUMAN; MELANOMA; OUTCOME ASSESSMENT; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; CLINICAL COMPETENCE; DIAGNOSTIC IMAGING; IMAGE PROCESSING; INTERNATIONAL COOPERATION; PROCEDURES; RETROSPECTIVE STUDY; SKIN; SKIN TUMOR;

CLINICAL COMPETENCE; CROSS-SECTIONAL STUDIES; DEEP LEARNING; DERMATOLOGISTS; DERMOSCOPY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INTERNATIONAL COOPERATION; MELANOMA; RETROSPECTIVE STUDIES; ROC CURVE; SKIN; SKIN NEOPLASMS;

EID: 85054158054     PISSN: 09237534     EISSN: 15698041     Source Type: Journal    
DOI: 10.1093/annonc/mdy166     Document Type: Article

References (21)

1. Koh HK. Melanoma screening: focusing the public health journey. Arch Dermatol 2007; 143(1): 101–103.
2. Nikolaou V, Stratigos AJ. Emerging trends in the epidemiology of melanoma. Br J Dermatol 2014; 170(1): 11–19.
3. Vestergaard ME, Macaskill P, Holt PE, Menzies SW. Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta-analysis of studies performed in a clinical setting. Br J Dermatol 2008; 159: 669–676.
4. Bafounta ML, Beauchet A, Aegerter P, Saiag P. Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. Arch Dermatol 2001; 137(10): 1343–1350.
5. Salerni G, Teran T, Puig S et al. Meta-analysis of digital dermoscopy follow-up of melanocytic skin lesions: a study on behalf of the International Dermoscopy Society. J Eur Acad Dermatol Venereol 2013; 27(7): 805–814.
6. Dolianitis C, Kelly J, Wolfe R, Simpson P. Comparative performance of 4 dermoscopic algorithms by nonexperts for the diagnosis of melanocytic lesions. Arch Dermatol 2005; 141(8): 1008–1014.
7. Carli P, Quercioli E, Sestini S et al. Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology. Br J Dermatol 2003; 148(5): 981–984.
8. Barata C, Celebi ME, Marques JS. Improving dermoscopy image classification using color constancy. IEEE J Biomed Health Inform 2015; 19: 1–52.
9. Glaister J, Wong A, Clausi DA. Segmentation of skin lesions from digital images using joint statistical texture distinctiveness. IEEE Trans Biomed Eng 2014; 61(4): 1220–1230.
10. Garnavi R, Aldeen M, Bailey J. Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis. IEEE Trans Inform Technol Biomed 2012; 16(6): 1239–1252.
11. Kaya S, Bayraktar M, Kockara S et al. Abrupt skin lesion border cutoff measurement for Malignancy detection in dermoscopy images. BMC Bioinformatics 2016; 17(S13): 367.
12. Pehamberger H, Steiner A, Wolff K. In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions. J Am Acad Dermatol 1987; 17(4): 571–583.
13. Esteva A, Kuprel B, Novoa RA et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542(7639): 115–118.
14. Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision 2015. https://arxiv.org/abs/1512.00567 (9 May 2018, date last accessed).
15. Marchetti MA, Codella NCF, Dusza SW et al. 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. J Am Acad Dermatol 2018; 78(2): 270–277.
16. Menzies SW, Gutenev A, Avramidis M et al. Short-term digital surface microscopic monitoring of atypical or changing melanocytic lesions. Arch Dermatol 2001; 137(12): 1583–1589.
17. Altamura D, Avramidis M, Menzies SW. Assessment of the optimal interval for and sensitivity of short-term sequential digital dermoscopy monitoring for the diagnosis of melanoma. Arch Dermatol 2008; 144(4): 502–506.
18. Menzies SW, Emery J, Staples M et al. Impact of dermoscopy and short-term sequential digital dermoscopy imaging for the management of pigmented lesions in primary care: a sequential intervention trial. Br J Dermatol 2009; 161(6): 1270–1277.
19. Menzies SW, Stevenson ML, Altamura D, Byth K. Variables predicting change in benign melanocytic nevi undergoing short-term dermoscopic imaging. Arch Dermatol 2011; 147(6): 655–659.
20. Ferris LK, Harkes JA, Gilbert B et al. Computer-aided classification of melanocytic lesions using dermoscopic images. J Am Acad Dermatol 2015; 73(5): 769–776.
21. Hauschild A, Chen SC, Weichenthal M et al. To excise or not: impact of MelaFind on German dermatologists’ decisions to biopsy atypical lesions. J Dtsch Dermatol Ges 2014; 12(7): 606–614.