BCN20000: Dermoscopic Lesions in the Wild

Melanoma classification on dermoscopy images using a neural network ensemble model

Original

F. Xie, H. Fan, Y. Li, Z. Jiang, R. Meng, and A. Bovik · 1908

Earlier work this paper cites.

Diagnostic accuracy of dermoscopy

H. Kittler, H. Pehamberger, K. Wolff, and M. Binder · 2002

Earlier work this paper cites.

Dermoscopy of pigmented skin lesions: results of a consensus meeting via the internet

G. Argenziano, H. P. Soyer, S. Chimenti, R. Talamini, R. Corona, F. Sera, M. Binder, L. Cerroni, G. De Rosa, G. Ferrara, et al · 2003

Earlier work this paper cites.

Skin lesion analysis toward melanoma detection: A challenge at the international symposium on biomedical imaging (isbi) 2016, hosted by the international skin imaging collaboration (isic)

Original

D. Gutman, N. C. Codella, E. Celebi, B. Helba, M. Marchetti, N. Mishra, and A. Halpern · 2016

Earlier work this paper cites.

Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks

Original

L. Bi, J. Kim, E. Ahn, and D. Feng · 2017

Earlier work this paper cites.

Dermatologist-level classification of skin cancer with deep neural networks

A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun · 2017

Cited alongside, same era.

Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)

N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler, et al · 2018

Cited alongside, same era.

Results of the 2016 international skin imaging collaboration isbi challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

M. A. Marchetti, N. C. Codella, S. W. Dusza, D. A. Gutman, B. Helba, A. Kalloo, N. Mishra, C. Carrera, M. E. Celebi, J. L. DeFazio, et al · 2018

Cited alongside, same era.

The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

P. Tschandl, C. Rosendahl, and H. Kittler · 2018

Cited alongside, same era.

Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic)

Original

N. Codella, V. Rotemberg, P. Tschandl, M. E. Celebi, S. Dusza, D. Gutman, B. Helba, A. Kalloo, K. Liopyris, M. Marchetti, et al · 2019

Closest in time.

https://www.isic-archive.com/, 2019

ISICArchive · 2019

Closest in time.

https://challenge2019.isic-archive.com/, 2019

ISICChallenge2019 · 2019

Closest in time.

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

P. Tschandl, N. Codella, B. N. Akay, G. Argenziano, R. P. Braun, H. Cabo, D. Gutman, A. Halpern, B. Helba, R. Hofmann-Wellenhof, et al · 2019

Closest in time.