  In this article, we describe the design and implementation of a publicly
accessible dermatology image analysis benchmark challenge. The goal of the
challenge is to sup- port research and development of algorithms for automated
diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images.
The challenge was divided into sub-challenges for each task involved in image
analysis, including lesion segmentation, dermoscopic feature detection within a
lesion, and classification of melanoma. Training data included 900 images. A
separate test dataset of 379 images was provided to measure resultant
performance of systems developed with the training data. Ground truth for both
training and test sets was generated by a panel of dermoscopic experts. In
total, there were 79 submissions from a group of 38 participants, making this
the largest standardized and comparative study for melanoma diagnosis in
dermoscopic images to date. While the official challenge duration and ranking
of participants has concluded, the datasets remain available for further
research and development.
