{"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/rwkv-unet-improving-unet-with-long-range","title":"RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation","arxiv_id":"2501.08458","date":"2025-01-14","proceeding":null,"authors":["Juntao Jiang","Jiangning Zhang","Weixuan Liu","Muxuan Gao","Xiaobin Hu","Xiaoxiao Yan","Feiyue Huang","Yong liu"],"abstract":"In recent years, there have been significant advancements in deep learning for medical image analysis, especially with convolutional neural networks (CNNs) and transformer models. However, CNNs face limitations in capturing long-range dependencies while transformers suffer high computational complexities. To address this, we propose RWKV-UNet, a novel model that integrates the RWKV (Receptance Weighted Key Value) structure into the U-Net architecture. This integration enhances the model's ability to capture long-range dependencies and improve contextual understanding, which is crucial for accurate medical image segmentation. We build a strong encoder with developed inverted residual RWKV (IR-RWKV) blocks combining CNNs and RWKVs. We also propose a Cross-Channel Mix (CCM) module to improve skip connections with multi-scale feature fusion, achieving global channel information integration. Experiments on benchmark datasets, including Synapse, ACDC, BUSI, CVC-ClinicDB, CVC-ColonDB, Kvasir-SEG, ISIC 2017 and GLAS show that RWKV-UNet achieves state-of-the-art performance on various types of medical image segmentation. Additionally, smaller variants, RWKV-UNet-S and RWKV-UNet-T, balance accuracy and computational efficiency, making them suitable for broader clinical applications.","url_abs":"https://arxiv.org/abs/2501.08458v1","url_pdf":"https://arxiv.org/pdf/2501.08458v1.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":"rwkv-unet-improving-unet-with-long-range","repo_url":"https://github.com/juntaojianggavin/rwkv-unet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-image-segmentation-on-acdc","task":"Medical Image Segmentation","dataset":"ACDC","model":"RWKV-UNet","rank_in_archive_order":3,"of":6,"metrics":{"Dice Score":"0.9217"},"uses_additional_data":false},{"leaderboard":"/sota/medical-image-segmentation-on-miccai-2015-1","task":"Medical Image Segmentation","dataset":"MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge","model":"RWKV-UNet","rank_in_archive_order":3,"of":8,"metrics":{"Avg DSC":"84.02","Avg HD":"15.7"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}