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WSSM: A Weakly Supervised Oral Mucosal Disease Segmentation Model Based on Multi-Task Collaboration.

Jing Xu1, Jianguo Ju2, Qian Zhang1

  • 1College of Computer Science, Northwest University, Xi'an, China.

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|January 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a weakly supervised oral mucosal disease (OMD) segmentation model (WSSM) that enhances diagnostic accuracy. WSSM improves lesion boundary segmentation, addressing limitations in traditional OMD diagnosis.

Keywords:
deep learningdepthwise separable convolutionmedical image segmentationoral mucosal diseasestate space modelweakly supervised

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Traditional oral mucosal disease (OMD) diagnosis is subjective and inefficient, relying on visual assessment.
  • OMD imaging lacks sufficient supervision and has fuzzy lesion boundaries, hindering mobile medicine applications.

Purpose of the Study:

  • To develop a weakly supervised OMD segmentation model (WSSM) to improve diagnostic accuracy and efficiency.
  • To address challenges of insufficient supervision and fuzzy boundaries in OMD image analysis.

Main Methods:

  • Proposed a weakly supervised OMD segmentation model (WSSM) with a Mamba backbone and dual-branch collaboration.
  • Implemented a classification branch for multi-scale feature extraction and a pseudo-label module for deeper supervision.
  • Utilized a segmentation branch with a boundary adaptive module to enhance fuzzy boundary representation.

Main Results:

  • WSSM significantly outperformed existing weakly supervised methods on the OMD dataset.
  • Achieved a 6.06% increase in Dice index compared to WSSL, demonstrating superior segmentation performance.

Conclusions:

  • The Mamba-based WSSM balances local texture and long-range dependencies for OMD lesion analysis.
  • Dual-branch collaboration and deeper supervision significantly improve boundary segmentation accuracy for OMDs with unclear boundaries.