This work summarizes the results of the largest skin image analysis challenge
in the world, hosted by the International Skin Imaging Collaboration (ISIC), a
global partnership that has organized the world's largest public repository of
dermoscopic images of skin. The challenge was hosted in 2018 at the Medical
Image Computing and Computer Assisted Intervention (MICCAI) conference in
Granada, Spain. The dataset included over 12,500 images across 3 tasks. 900
users registered for data download, 115 submitted to the lesion segmentation
task, 25 submitted to the lesion attribute detection task, and 159 submitted to
the disease classification task. Novel evaluation protocols were established,
including a new test for segmentation algorithm performance, and a test for
algorithm ability to generalize. Results show that top segmentation algorithms
still fail on over 10% of images on average, and algorithms with equal
performance on test data can have different abilities to generalize. This is an
important consideration for agencies regulating the growing set of machine
learning tools in the healthcare domain, and sets a new standard for future
public challenges in healthcare.

本研究总结了由国际皮肤成像协作组织（ISIC）主办的全球最大的皮肤图像分析挑战赛的结果，旨在建立皮肤图像智能分析的新标准，实验表明，当前前沿的皮肤图像智能分析仍存在诊断错误率较高的问题以及算法能力泛化差异问题，这对于卫生保健领域中的机器学习工具监管机构来说是一个重要考虑。