Keywords: Self-supervised, melanoma, ugly duckling, outlier
TL;DR: Self-supervised algorithms for ugly duckling scoring
Abstract: Screening skin lesions is a very time-consuming process in which the dermatologist examines hundreds of lesions all over the patient's body in a limited period of time. The decision as to which lesions should be further examined is made based on the "ugly duckling" sign. The dermatologist compares all lesions on the same patient and identifies those that are different from the average-looking lesions. Deep learning algorithms have been shown to be efficient tools for detecting outliers in large image datasets. In this study, we propose a self-supervised approach for lesion clustering and outlier detection to identify and suggest lesions of interest for each individual patient.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Application: Dermatology
Secondary Subject Area: Unsupervised Learning and Representation Learning
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