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

Koh, 2007, Melanoma screening: focusing the public health journey, Arch Dermatol, 143, 101, 10.1001/archderm.143.1.101

Nikolaou, 2014, Emerging trends in the epidemiology of melanoma, Br J Dermatol, 170, 11, 10.1111/bjd.12492

Vestergaard, 2008, 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, 159, 669

Bafounta, 2001, 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, 137, 1343, 10.1001/archderm.137.10.1343

Salerni, 2013, 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, 27, 805, 10.1111/jdv.12032

Dolianitis, 2005, Comparative performance of 4 dermoscopic algorithms by nonexperts for the diagnosis of melanocytic lesions, Arch Dermatol, 141, 1008, 10.1001/archderm.141.8.1008

Carli, 2003, Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology, Br J Dermatol, 148, 981, 10.1046/j.1365-2133.2003.05023.x

Barata, 2015, Improving dermoscopy image classification using color constancy, IEEE J Biomed Health Inform, 19, 1

Glaister, 2014, Segmentation of skin lesions from digital images using joint statistical texture distinctiveness, IEEE Trans Biomed Eng, 61, 1220, 10.1109/TBME.2013.2297622

Garnavi, 2012, Computer-aided diagnosis of melanoma using border and wavelet-based texture analysis, IEEE Trans Inform Technol Biomed, 16, 1239, 10.1109/TITB.2012.2212282

Kaya, 2016, Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images, BMC Bioinformatics, 17, 367, 10.1186/s12859-016-1221-4

Pehamberger, 1987, In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions, J Am Acad Dermatol, 17, 571, 10.1016/S0190-9622(87)70239-4

Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056

C Szegedy, V Vanhoucke, S Ioffe et al. Rethinking the inception architecture for computer vision 2015. https://arxiv.org/abs/1512.00567 (9 May 2018, date last accessed).

Marchetti, 2018, 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, 78, 270, 10.1016/j.jaad.2017.08.016

Menzies, 2001, Short-term digital surface microscopic monitoring of atypical or changing melanocytic lesions, Arch Dermatol, 137, 1583, 10.1001/archderm.137.12.1583

Altamura, 2008, Assessment of the optimal interval for and sensitivity of short-term sequential digital dermoscopy monitoring for the diagnosis of melanoma, Arch Dermatol, 144, 502, 10.1001/archderm.144.4.502

Menzies, 2009, 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, 161, 1270, 10.1111/j.1365-2133.2009.09374.x

Menzies, 2011, Variables predicting change in benign melanocytic nevi undergoing short-term dermoscopic imaging, Arch Dermatol, 147, 655, 10.1001/archdermatol.2011.133

Ferris, 2015, Computer-aided classification of melanocytic lesions using dermoscopic images, J Am Acad Dermatol, 73, 769, 10.1016/j.jaad.2015.07.028

Hauschild, 2014, To excise or not: impact of MelaFind on German dermatologists' decisions to biopsy atypical lesions, J Dtsch Dermatol Ges, 12, 606