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UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation

1 Aug 2024arXiv:2408.00273archive 2025-07-28

Yanbing Chen, Tianze Tang, Taehyo Kim, Hai Shu

Gliomas are among the most common malignant brain tumors and are characterized by considerable heterogeneity, which complicates accurate detection and segmentation. Multi-modal MRI is the clinical standard for glioma imaging, but variability across modalities and high computational complexity hinder effective automated segmentation. In this paper, we propose UKAN-EP, a novel 3D extension of the original 2D U-KAN model for multi-modal MRI brain tumor segmentation. While U-KAN integrates Kolmogorov-Arnold Network (KAN) layers into a U-Net backbone, UKAN-EP further incorporates Efficient Channel Attention (ECA) and Pyramid Feature Aggregation (PFA) modules to enhance inter-modality feature fusion and multi-scale feature representation. We also introduce a dynamic loss weighting strategy that adaptively balances the Cross-Entropy and Dice losses during training. We evaluate UKAN-EP on the 2024 BraTS-GLI dataset and compare it against strong baselines including U-Net, Attention U-Net, and Swin UNETR. Results show that UKAN-EP achieves superior segmentation performance while requiring substantially fewer computational resources. An extensive ablation study further demonstrates the effectiveness of ECA and PFA, as well as the limited utility of self-attention and spatial attention alternatives. Code is available at https://github.com/TianzeTang0504/UKAN-EP.

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Brain Tumor Segmentation Segmentation Tumor Segmentation

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1x1 Convolution Attention Batch Normalization Concatenated Skip Connection Convolution Dense Connections Efficient Channel Attention Global Average Pooling Linear Layer Max Pooling Multi-Head Attention Position-Wise Feed-Forward Layer ReLU Residual Connection Softmax U-Net UNETR

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