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KANDU-Net:A Dual-Channel U-Net with KAN for Medical Image Segmentation

arXiv 2409.20414 ↗First posted 30 Sep 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

The U-Net model has consistently demonstrated strong performance in the field of medical image segmentation, with various improvements and enhancements made since its introduction. This paper presents a novel architecture that integrates KAN networks with U-Net, leveraging the powerful nonlinear representation capabilities of KAN networks alongside the established strengths of U-Net. We introduce a KAN-convolution dual-channel structure that enables the model to more effectively capture both local and global features. We explore effective methods for fusing features extracted by KAN with those obtained through convolutional layers, utilizing an auxiliary network to facilitate this integration process. Experiments conducted across multiple datasets show that our model performs well in terms of accuracy, indicating that the KAN-convolution dual-channel approach has significant potential in medical image segmentation tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 37 references that are in the map. First posted on arXiv 30 Sep 2024.

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21 of 37 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
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  11. 2024U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentationcited by 412
  12. 2016Gland Segmentation in Colon Histology Images: The GlaS Challenge Contestcited by 203
9 more
  1. 2021UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformercited by 197
  2. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 176
  3. 2019Understanding Deep Learning Techniques for Image Segmentationcited by 38
  4. 2020DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentationcited by 28
  5. 2018MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentationcited by 27
  6. 2019D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentationcited by 24
  7. 2024SelfReg-UNet: Self-Regularized UNet for Medical Image Segmentationcited by 18
  8. 2017GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Networkcited by 7
  9. 2023Medical Image Segmentation via Sparse Coding Decodercited by 3

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