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Superpixel-Oriented Label Distribution Learning for Skin Lesion Segmentation.

Qiaoer Zhou1, Tingting He1, Yuanwen Zou1

  • 1College of Biomedical Engineering, Sichuan University, Chengdu 610065, China.

Diagnostics (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel superpixel-oriented label distribution learning method to improve skin lesion segmentation accuracy in deep learning models. The approach enhances segmentation performance by generating more reliable soft labels from uncertain or erroneous annotations.

Area of Science:

  • Medical Image Analysis
  • Artificial Intelligence in Dermatology
  • Computational Pathology

Background:

  • Accurate skin lesion segmentation is crucial for early skin cancer detection.
  • Deep learning models require high-quality, human-annotated data, which is often difficult to obtain for dermatological images due to label uncertainty and inter-observer variability.
  • Existing methods using one-hot labels can lead to overconfident and overfitted models, impacting segmentation performance.

Purpose of the Study:

  • To develop a robust method for improving skin lesion segmentation by addressing the challenge of uncertain and erroneous labels.
  • To introduce a superpixel-oriented label distribution learning approach that generates soft labels incorporating structural prior information.
  • To enhance the performance and reliability of deep learning models for dermoscopy image analysis.
Keywords:
label distribution learningsegmentationskin cancersoft labelssuperpixel

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Main Methods:

  • A superpixel-oriented label distribution learning method was proposed, utilizing the Simple Linear Iterative Clustering (SLIC) algorithm.
  • One-hot labels were constrained and converted into soft probability distributions using a defined distance function.
  • Knowledge distillation principles were applied to transfer structural prior information from soft labels to the lesion segmentation network.

Main Results:

  • The proposed method achieved a Dice coefficient of 84%, sensitivity of 79.6%, and precision of 80.4% on the ISIC 2018 dataset.
  • Significant improvements of 19.3% in Dice coefficient, 8.6% in sensitivity, and 2.5% in precision were observed compared to the U-Net baseline.
  • The method demonstrated improved performance across various general neural network architectures for skin lesion segmentation.

Conclusions:

  • The superpixel-oriented label distribution learning method effectively improves skin lesion segmentation accuracy by generating informative soft labels.
  • This approach mitigates issues related to label uncertainty and enhances model robustness against overfitting.
  • The proposed method is versatile and can be readily integrated into existing deep learning architectures for enhanced performance in skin cancer analysis.