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.

Background: Multiple studies have compared the performance of artificial intelligence (AI)ebased models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.

No abstract

CONFLICT OF INTEREST WL is employed by SK Telecom. But the company did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Synopsis Dermoscopy increases the sensitivity for skin cancer detection, decreases the number of benign lesions biopsied for each malignant diagnosis, and enables the diagnosis of thinner melanomas compared to naked eye examination. Three meta-analyses have all identified that dermoscopy improved diagnostic accuracy for melanoma when compared to naked eye exam. In addition, studies have established that dermoscopy can aid in the detection of keratinocyte carcinomas. Dermoscopy triage algorithms have been developed to help novices decide when a biopsy or a referral is most appropriate. In this chapter we illustrate the dermoscopic features that assist in identifying melanoma and keratinocyte carcinomas.