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An ugly duckling is an obviously different skin lesion from surroundinglesions of an individual, and the ugly duckling sign is a criterion used to aidin the diagnosis of cutaneous melanoma by differentiating between highlysuspicious and benign lesions. However, the appearance of pigmented lesions,can change drastically from one patient to another, resulting in difficultiesin visual separation of ugly ducklings. Hence, we propose DMT-Quadruplet - adeep metric learning network to learn lesion features at two tiers -patient-level and lesion-level. We introduce a patient-specific quadrupletmining approach together with a tiered quadruplet network, to drive the networkto learn more contextual information both globally and locally between the twotiers. We further incorporate a dynamic margin within the patient-specificmining to allow more useful quadruplets to be mined within individuals.Comprehensive experiments show that our proposed method outperforms traditionalclassifiers, achieving 54% higher sensitivity than a baseline ResNet18 CNN and37% higher than a naive triplet network in classifying ugly duckling lesions.Visualisation of the data manifold in the metric space further illustrates thatDMT-Quadruplet is capable of classifying ugly duckling lesions in bothpatient-specific and patient-agnostic manner successfully.

Ugly Ducklings or Swans: A Tiered Quadruplet Network with Patient-Specific Mining for Improved Skin Lesion Classification