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TNCB: Tri-Net With Cross-Balanced Pseudo Supervision for Class Imbalanced Medical Image Classification.

Aixi Qu, Qiang Wu, Jing Wang

    IEEE Journal of Biomedical and Health Informatics
    |February 8, 2024
    PubMed
    Summary

    Deep neural networks in medical imaging face challenges with limited labeled data and imbalanced classes. The proposed Tri-Net with Cross-Balanced pseudo supervision (TNCB) framework effectively addresses these issues, improving classification accuracy.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep neural networks (DNNs) are crucial for medical image analysis but struggle with limited labeled data and class imbalance.
    • Existing semi-supervised learning (SSL) methods often fail to address class imbalance, leading to prediction bias towards majority classes.
    • Training bias also arises from suboptimal strategies in pseudo-label generation and utilization within current SSL frameworks.

    Purpose of the Study:

    • To introduce a novel semi-supervised learning (SSL) framework, Tri-Net with Cross-Balanced pseudo supervision (TNCB), designed to overcome label scarcity and class imbalance in medical image classification.
    • To reduce prediction bias by enabling the teacher model to focus on minority classes.
    • To mitigate training bias through an adaptive cross-loss function for improved knowledge extraction from unlabeled data.

    Main Methods:

    • Proposed the Tri-Net with Cross-Balanced pseudo supervision (TNCB) framework, comprising two student networks and a teacher network with an adaptive balancer.
    • Implemented a virtual optimization strategy to enhance the teacher model's robustness against class imbalance.
    • Utilized cross-balanced pseudo supervision with an adaptive cross-loss function to minimize training bias and leverage unlabeled data.

    Main Results:

    • TNCB demonstrated superior performance compared to state-of-the-art SSL methods across four diverse datasets.
    • The framework effectively addressed challenges related to different diseases, image modalities, and varying imbalance ratios.
    • Consistent improvements in classification accuracy highlight the effectiveness of TNCB.

    Conclusions:

    • TNCB is an effective and robust SSL framework for imbalanced medical image classification.
    • The proposed methods successfully reduce both prediction and training bias inherent in existing SSL approaches.
    • TNCB offers a promising solution for enhancing deep learning applications in clinical settings with limited and imbalanced medical data.