Flexible sampling for long-tailed skin lesion classification

Lie Ju, Yicheng Wu, Lin Wang, Zhen Yu, Xin Zhao, Xin Wang, Paul Bonnington, Zongyuan Ge

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

3 Citations (Scopus)


Most of the medical tasks naturally exhibit a long-tailed distribution due to the complex patient-level conditions and the existence of rare diseases. Existing long-tailed learning methods usually treat each class equally to re-balance the long-tailed distribution. However, considering that some challenging classes may present diverse intra-class distributions, re-balancing all classes equally may lead to a significant performance drop. To address this, in this paper, we propose a curriculum learning-based framework called Flexible Sampling for the long-tailed skin lesion classification task. Specifically, we initially sample a subset of training data as anchor points based on the individual class prototypes. Then, these anchor points are used to pre-train an inference model to evaluate the per-class learning difficulty. Finally, we use a curriculum sampling module to dynamically query new samples from the rest training samples with the learning difficulty-aware sampling probability. We evaluated our model against several state-of-the-art methods on the ISIC dataset. The results with two long-tailed settings have demonstrated the superiority of our proposed training strategy, which achieves a new benchmark for long-tailed skin lesion classification.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2022
Subtitle of host publication25th International Conference, Singapore, September 18–22, 2022, Proceedings, Part III
EditorsLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
Place of PublicationCham Switzerland
Number of pages10
ISBN (Electronic)9783031164378
ISBN (Print)9783031164361
Publication statusPublished - 2022
EventMedical Image Computing and Computer-Assisted Intervention 2022 - Singapore, Singapore
Duration: 18 Sept 202222 Sept 2022
Conference number: 25th
https://link.springer.com/book/10.1007/978-3-031-16434-7 (Proceedings - Part 2)
https://conferences.miccai.org/2022/en/ (Website)

Publication series

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


ConferenceMedical Image Computing and Computer-Assisted Intervention 2022
Abbreviated titleMICCAI 2022
Internet address


  • Flexible sampling
  • Long-tailed classification
  • Skin lesion

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