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Updated: Jan 10, 2026

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Channel-Gated Transformers With Affinity CAM for Weakly Supervised Multi-Class Brain Tumor Segmentation.

Yan Han, Kai Liu, Lingling Yuan

    IEEE Journal of Biomedical and Health Informatics
    |November 24, 2025
    PubMed
    Summary
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    This study introduces Channel-gated Transformers with Affinity CAM (CTAC) for improved multi-class brain tumor segmentation. CTAC enhances sub-region discrimination and reduces over-segmentation, significantly outperforming existing methods on benchmark datasets.

    Area of Science:

    • Medical image analysis
    • Artificial intelligence in healthcare
    • Computational neuroscience

    Background:

    • Accurate brain tumor segmentation is crucial for diagnosis and treatment planning.
    • Current weakly supervised semantic segmentation (WSSS) methods struggle with multi-class brain tumor segmentation, particularly differentiating sub-regions and handling small lesions.
    • Transformer-based WSSS methods face challenges like over-smoothing and over-segmentation in multi-class brain tumor scenarios.

    Purpose of the Study:

    • To develop an advanced multi-class weakly supervised semantic segmentation (WSSS) method for brain tumor analysis.
    • To address the limitations of existing transformer-based WSSS methods in differentiating tumor sub-regions and mitigating over-segmentation.
    • To propose a novel approach, Channel-gated Transformers with Affinity CAM (CTAC), for enhanced brain tumor segmentation.

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    Main Methods:

    • Proposed Channel-gated Transformers with Affinity CAM (CTAC) model for multi-class brain tumor segmentation.
    • Employed channel-gated multi-head self-attention to enhance inter-class discriminability and overcome transformer over-smoothing.
    • Utilized multi-scale smoothed affinity to suppress low-confidence responses in Class Activation Maps (CAM), reducing over-segmentation of small lesions.

    Main Results:

    • CTAC significantly outperformed the baseline method on the BraTS2021 glioma and BraTS2023-MEN meningioma datasets.
    • Achieved a multi-class mean IoU (mIoU) of 61.718% on BraTS2021 (+4.964 pp) and 72.887% on BraTS2023-MEN (+4.676 pp).
    • Demonstrated superior performance compared to recent state-of-the-art methods in brain tumor segmentation.

    Conclusions:

    • CTAC effectively addresses the challenges of sub-region discrimination and over-segmentation in multi-class brain tumor WSSS.
    • The proposed channel-gated attention and affinity CAM mechanisms enhance segmentation accuracy and robustness.
    • CTAC represents a significant advancement in weakly supervised semantic segmentation for brain tumor analysis.