One commonly used clinical approach towards detecting melanomas recognises
the existence of Ugly Duckling nevi, or skin lesions which look different from
the other lesions on the same patient. An automatic method of detecting and
analysing these lesions would help to standardize studies, compared with manual
screening methods. However, it is difficult to obtain expertly-labelled images
for ugly duckling lesions. We therefore propose to use self-supervised machine
learning to automatically detect outlier lesions. We first automatically detect
and extract all the lesions from a wide-field skin image, and calculate an
embedding for each detected lesion in a patient image, based on automatically
identified features. These embeddings are then used to calculate the L2
distances as a way to measure dissimilarity. Using this deep learning method,
Ugly Ducklings are identified as outliers which should deserve more attention
from the examining physician. We evaluate through comparison with
dermatologists, and achieve a sensitivity rate of 72.1% and diagnostic accuracy
of 94.2% on the held-out test set.

本研究提出了一种利用自监督机器学习自动检测异常皮损的方法，通过测量 L2 距离来识别丑小鸭痣。该方法在测试集中实现了 72.1% 的敏感度和 94.2% 的诊断准确度，具有较高的应用潜力。