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Related Experiment Video

Updated: Dec 13, 2025

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

3.2K

Semi-Supervised Medical Image Classification With Relation-Driven Self-Ensembling Model.

Quande Liu, Lequan Yu, Luyang Luo

    IEEE Transactions on Medical Imaging
    |August 4, 2020
    PubMed
    Summary
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    This study introduces a new semi-supervised learning framework for medical image classification that uses sample relation consistency (SRC) to improve performance with limited labeled data. The novel approach effectively leverages unlabeled data by modeling relationships between samples, outperforming existing methods.

    Area of Science:

    • Artificial Intelligence
    • Medical Image Analysis
    • Machine Learning

    Background:

    • Deep neural networks require extensive labeled data for optimal performance.
    • Acquiring labeled medical images is challenging due to the need for expert clinical knowledge, making it costly and time-consuming.
    • Semi-supervised learning offers a promising solution to mitigate the reliance on large labeled datasets in medical imaging.

    Purpose of the Study:

    • To develop a novel relation-driven semi-supervised framework for medical image classification.
    • To enhance the utilization of unlabeled data by introducing a sample relation consistency (SRC) paradigm.
    • To improve the accuracy of medical image classification models in scenarios with limited labeled data.

    Main Methods:

    • A consistency-based semi-supervised learning framework was developed, exploiting unlabeled data through prediction consistency under perturbations.

    Related Experiment Videos

    Last Updated: Dec 13, 2025

    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

    3.2K
  • A self-ensembling model was employed to generate high-quality consistency targets for unlabeled data.
  • A novel sample relation consistency (SRC) paradigm was introduced to model relationships among samples, enforcing consistency of semantic relations under perturbations.

    • Main Results:

    • The proposed framework demonstrated superior performance compared to existing state-of-the-art semi-supervised learning methods.
    • Experiments were conducted on benchmark datasets for skin lesion diagnosis (ISIC 2018) and thorax disease classification (ChestX-ray14).
    • The method achieved high accuracy in both single-label and multi-label medical image classification tasks.

    Conclusions:

    • The novel relation-driven semi-supervised framework effectively leverages unlabeled data through sample relation consistency.
    • The approach offers a significant advancement in medical image classification, particularly when labeled data is scarce.
    • This method provides a robust solution for improving diagnostic accuracy in clinical settings.