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

Precise tumor localization and sub-region identification are critical for disease diagnosis. However, current Weakly Supervised Semantic Segmentation (WSSS) methods for brain tumor segmentation are primarily single-class, neglecting differences between tumor sub-regions. We observed that when mainstream transformer-based WSSS methods are applied to multi-class brain tumor segmentation, they encounter two major challenges: sub-region discrimination errors and over-segmentation of small lesions. To address these challenges and advance multi-class WSSS methods for brain tumor analysis, this paper proposes Channel-gated Transformers with Affinity CAM (CTAC). CTAC first employs channel-gated multi-head self-attention to overcome the over-smoothing tendency of the transformer, thereby enhancing inter-class discriminability and improving the model's subclass differentiation capability. Then, CTAC uses multi-scale smoothed affinity to adaptively suppress low-confidence responses in the Class Activation Map (CAM), mitigating over-activation in the CAM, and alleviating the over-segmentation phenomena of small lesions. The proposed CTAC significantly outperformed the baseline method on the BraTS2021 glioma and BraTS2023-MEN meningioma datasets. On Brats2021, it achieved a multi-class mean IoU (mIoU) of 61.718%, an increase of 4.964 percentage points (pp), with the whole-tumor mIoU reaching 79.798% (+6.882 pp). On Brats2023-MEN, CTAC attained 72.887% mIoU (+4.676 pp) for multi-class segmentation and 75.394% (+7.839 pp) for whole-tumor. Furthermore, CTAC surpasses recent state-of-the-art methods. Code is available at https://github.com/yhan94-lab/CTAC.