mlsanityData to 7 Oct 2026

RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation

arXiv 2501.08458 ↗First posted 14 Jan 2025eess.IV cs.CVcited by 7

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Abstract (arXiv)

In recent years, significant advancements have been made in deep learning for medical image segmentation, particularly with convolutional neural networks (CNNs) and transformer models. However, CNNs face limitations in capturing long-range dependencies, while transformers suffer from high computational complexity. 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 to improve contextual understanding, which is crucial for accurate medical image segmentation. We build a strong encoder with developed Global-Local Spatial Perception (GLSP) 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 11 benchmark datasets show that the RWKV-UNet achieves state-of-the-art performance on various types of medical image segmentation tasks. Additionally, smaller variants, RWKV-UNet-S and RWKV-UNet-T, balance accuracy and computational efficiency, making them suitable for broader clinical applications.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 55 of its 75 references that are in the map. First posted on arXiv 14 Jan 2025.

Builds on

55 of 75 references.

References · 55

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2026ProSMA-UNet: Decoder Conditioning for Proximal-Sparse Skip Feature Selectioncited by 1
  2. 2026Efficiency Follows Global-Local Decouplingcited by 1
  3. 2025RDTE-UNet: A Boundary and Detail Aware UNet for Precise Medical Image Segmentationcited by 0
  4. 2025MACMD: Multi-dilated Contextual Attention and Channel Mixer Decoding for Medical Image Segmentationcited by 0
  5. 2026Vascular anatomy-aware self-supervised pre-training for X-ray angiogram analysiscited by 0
  6. 2026Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentationcited by 0
  7. 2026APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigmscited by 0