Ugly Ducklings or Swans: A Tiered Quadruplet Network with
  Patient-Specific Mining for Improved Skin Lesion Classification
Nathasha Naranpanawa H. Peter Soyer Adam Mothershaw Gayan K. Kulatilleke Zongyuan Ge Brigid Betz-Stablein Shekhar S. Chandra
An ugly duckling is an obviously different skin lesion from surrounding
lesions of an individual, and the ugly duckling sign is a criterion used to aid
in the diagnosis of cutaneous melanoma by differentiating between highly
suspicious and benign lesions. However, the appearance of pigmented lesions,
can change drastically from one patient to another, resulting in difficulties
in visual separation of ugly ducklings. Hence, we propose DMT-Quadruplet - a
deep metric learning network to learn lesion features at two tiers -
patient-level and lesion-level. We introduce a patient-specific quadruplet
mining approach together with a tiered quadruplet network, to drive the network
to learn more contextual information both globally and locally between the two
tiers. We further incorporate a dynamic margin within the patient-specific
mining to allow more useful quadruplets to be mined within individuals.
Comprehensive experiments show that our proposed method outperforms traditional
classifiers, achieving 54% higher sensitivity than a baseline ResNet18 CNN and
37% higher than a naive triplet network in classifying ugly duckling lesions.
Visualisation of the data manifold in the metric space further illustrates that
DMT-Quadruplet is capable of classifying ugly duckling lesions in both
patient-specific and patient-agnostic manner successfully.