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In this article, we describe the design and implementation of a publiclyaccessible dermatology image analysis benchmark challenge. The goal of thechallenge is to sup- port research and development of algorithms for automateddiagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images.The challenge was divided into sub-challenges for each task involved in imageanalysis, including lesion segmentation, dermoscopic feature detection within alesion, and classification of melanoma. Training data included 900 images. Aseparate test dataset of 379 images was provided to measure resultantperformance of systems developed with the training data. Ground truth for bothtraining and test sets was generated by a panel of dermoscopic experts. Intotal, there were 79 submissions from a group of 38 participants, making thisthe largest standardized and comparative study for melanoma diagnosis indermoscopic images to date. While the official challenge duration and rankingof participants has concluded, the datasets remain available for furtherresearch and development.

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)