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)

TL;DR

This paper presents a comprehensive dermatology image analysis benchmark challenge aimed at advancing automated melanoma diagnosis through segmentation, feature detection, and classification tasks, supported by expert-annotated datasets and a large participant base.

Contribution

It introduces a publicly accessible benchmark with standardized datasets and tasks for melanoma detection, fostering research and comparison of algorithms in dermoscopic image analysis.

Findings

79 submissions from 38 participants evaluated.

Largest standardized study for melanoma diagnosis in dermoscopic images.

Datasets remain available for ongoing research.

Abstract

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.