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Related Experiment Video

Updated: Jun 30, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Dual contextual learning for semi-supervised medical image classification.

Jiaying Liu1, Chengyang Li2, Sangsha Fang3

  • 1Hunan University of Chinese Medicine, Changsha, China.

Frontiers in Medicine
|June 5, 2026
PubMed
Summary
This summary is machine-generated.

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Semi-supervised learning (SSL) improves medical image classification by using Hierarchical Semantic Calibration (HSC). This novel framework enhances pseudo-labeling reliability, leading to more accurate disease identification with limited data.

Area of Science:

  • Artificial Intelligence
  • Medical Image Analysis
  • Computer Vision

Background:

  • Semi-supervised learning (SSL) is crucial for medical image classification due to limited labeled data.
  • Existing pseudo-labeling methods struggle with ambiguous cases, leading to error accumulation.
  • Medical images possess rich contextual information that can improve classification accuracy.

Purpose of the Study:

  • To propose a Hierarchical Semantic Calibration (HSC) framework to enhance pseudo-labeling reliability in medical image classification.
  • To leverage contextual relationships within medical image data for more robust supervision.
  • To improve accuracy in classifying medical images, especially in challenging cases with limited annotations.

Main Methods:

  • Introduced a local semantic neighborhood alignment module to enforce consistency among k-nearest neighbors.
Keywords:
contrastive learningimage classificationimage processingmedical image analysissemi-supervised learning

Related Experiment Videos

Last Updated: Jun 30, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

  • Implemented a global cluster-prototype calibration module using contrastive learning for class-level representation alignment.
  • Developed a neighborhood-prototype consistency regularization to bridge local and global scales adaptively.

    • Main Results:

    • Achieved 92.24% accuracy on NCT-CRC-HE with only 200 labeled samples, outperforming existing methods.
    • Attained 94.17% accuracy on ISIC2018 using 20% labeled data, demonstrating significant improvement.
    • HSC consistently outperformed state-of-the-art methods in medical image classification tasks.

    Conclusions:

    • The proposed HSC framework effectively enhances pseudo-labeling reliability by utilizing hierarchical semantic information.
    • HSC demonstrates superior performance in medical image classification, particularly with limited labeled data.
    • The method offers a robust solution for accurate disease pattern recognition despite imaging variations and ambiguous features.