End-to-End Ugly Duckling Sign Detection for Melanoma Identification with Transformers

Zhen Yu, Victoria Mar, Anders Eriksson, Shakes Chandra, Paul Bonnington, Lei Zhang, Zongyuan Ge

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

6 Citations (Scopus)


The concept of ugly ducklings was introduced in dermatology to improve the likelihood of detecting melanoma by comparing a suspicious lesion against its surrounding lesions. The ugly duckling sign suggests nevi in the same individual tend to resemble one another while malignant melanoma often deviates from this nevus pattern. Differentiating the ugly duckling sign was more discriminatory between malignant melanoma and other nevi than quantitatively assessing dermoscopic patterns. In this study, we propose a framework for modeling ugly duckling context in melanoma identification (called UDTR hereafter). To this end, we construct our model in three parts: Firstly, we extract multi-scale features using a deep neural network from lesions in the same individuals; Then, we learn lesion context by modeling the dependency among features of lesions using a transformer encoder; Finally, we design a two branch architecture for performing both patient-level prediction and lesion-level prediction concurrently. Also, we propose a group contrastive learning strategy to enforce a large margin between benign and malignant lesions in feature space for better contextual feature learning. We evaluate our method on ISIC 2020 dataset which consists of ∼ 30,000 images from ∼ 2,000 patients. Extensive experiments evidence the effectiveness of our approach and highlight the importance of detecting lesions with clues from surrounding lesions than that of only evaluating lesion in question.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
Place of PublicationCham Switzerland
Number of pages9
ISBN (Electronic)9783030871994
ISBN (Print)9783030872335
Publication statusPublished - 2021
EventMedical Image Computing and Computer-Assisted Intervention 2021 - Online, Strasbourg, France
Duration: 27 Sept 20211 Oct 2021
Conference number: 24th
https://link.springer.com/book/10.1007/978-3-030-87196-3 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12907 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceMedical Image Computing and Computer-Assisted Intervention 2021
Abbreviated titleMICCAI 2021
Internet address


  • Deep learning
  • Melanoma diagnosis
  • Ugly duckling sign

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