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Related Experiment Video

Updated: Jun 12, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

A self-supervised pre-training method for lesion segmentation in oral potentially malignant disorders.

Yuqi Cao1, Miao Lu2, Jiayuan Zhang2

  • 1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China. yuqicao@zju.edu.cn.

BMC Oral Health
|June 11, 2026
PubMed
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This study introduces a self-guided pre-training method for oral potentially malignant disorder (OPMD) image segmentation, reducing the need for costly annotated data. The novel approach significantly improves OPMD lesion segmentation accuracy, even in data-scarce regions.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Supervised training for oral potentially malignant disorder (OPMD) image segmentation demands extensive annotated data, which is often scarce, especially in remote areas with limited medical specialists.
  • This scarcity poses a significant challenge for developing accurate and reliable OPMD detection systems.

Purpose of the Study:

  • To propose and evaluate a self-guided pre-training method for OPMD image segmentation.
  • To enhance the performance of OPMD lesion segmentation by automatically extracting features from unlabeled images, thereby reducing the reliance on expensive pixel-level annotations.

Main Methods:

  • Utilized a large internal dataset (3,417 OPMD photographs) and two external datasets for validation.
  • Developed a multiscale separable attention masked image model (MS-SAMIM) combined with a self-guided mask generation method.
Keywords:
Masked image modelingOral potentially malignant disorderSelf-supervised learningSemantic segmentation

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  • Incorporated teacher-student consistency and contrastive learning constraints to improve model robustness.

    • Main Results:

    • Achieved high performance in OPMD lesion segmentation: 84.35% Dice, 74.65% IoU, 81.84% sensitivity, and 88.02% precision on the internal dataset.
    • Demonstrated strong performance on external validation datasets, with Dice scores above 81% and IoU scores above 69%.
    • Outperformed various advanced fully supervised and other self-supervised segmentation approaches.

    Conclusions:

    • The proposed self-guided pre-training strategy effectively reduces the dependency on pixel-level annotations for OPMD lesion segmentation from clinical photographs.
    • The method shows promise for improving OPMD detection in resource-limited settings.
    • Further prospective validation is recommended prior to clinical implementation.