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Related Experiment Videos

TailBoost: Tail-Synthetic Learning for Boosting Long-Tailed Skin Cancer Image Classification.

Tianyunxi Wei1, Yijin Huang1,2, Li Lin1,3

  • 1Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces TailBoost, a novel framework for skin cancer image classification. TailBoost effectively addresses data imbalance in deep learning models by synthesizing minority class images using saliency maps, improving diagnostic accuracy.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Science

Background:

  • Skin cancer image datasets often have imbalanced class distributions, with few dominant classes and many underrepresented 'tail' classes.
  • This imbalance negatively impacts deep learning model performance in classifying skin lesions.
  • Existing methods like traditional mixup can distort important diagnostic features by not focusing on regions of interest.

Purpose of the Study:

  • To develop a novel framework, TailBoost, to improve long-tailed skin cancer image classification.
  • To address the limitations of traditional mixup techniques in preserving diagnostic features.
  • To enhance the performance of deep learning models on imbalanced skin cancer datasets.

Main Methods:

  • Introduced the TailBoost framework utilizing a novel strategy called SPMix.
Keywords:
long-tailed learningmixupsaliency mapskin cancer recognitionsupervised contrastive learning

Related Experiment Videos

  • SPMix generates synthetic tail-class images by combining tail-class and head-class images guided by saliency maps.
  • Incorporated supervised contrastive learning with class-center rebalance to refine learned representations.

    • Main Results:

    • TailBoost demonstrated superior performance compared to existing state-of-the-art long-tailed learning methods.
    • Experiments were conducted on ISIC2018, ISIC2019, and PAD-UFES-20 datasets.
    • The SPMix strategy effectively preserved and enhanced discriminative features of tail-class images.

    Conclusions:

    • TailBoost offers an effective solution for long-tailed skin cancer image classification.
    • The proposed SPMix method successfully mitigates feature distortion in synthetic data.
    • This framework significantly improves the accuracy of deep learning models in identifying rare skin cancer types.