{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/3d-u-kan-implementation-for-multi-modal-mri","title":"UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation","arxiv_id":"2408.00273","date":"2024-08-01","proceeding":null,"authors":["Yanbing Chen","Tianze Tang","Taehyo Kim","Hai Shu"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2408.00273v2","url_pdf":"https://arxiv.org/pdf/2408.00273v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"3d-u-kan-implementation-for-multi-modal-mri","repo_url":"https://github.com/tianzetang0504/ukan-ep","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"ecanet","method_name":"Efficient Channel Attention"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"u-net","method_name":"U-Net"},{"method_slug":"unetr","method_name":"UNETR"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}