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ResU-KAN: a medical image segmentation model integrating residual convolutional attention and atrous spatial pyramid pooling

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Abstract

With the rapid growth of medical imaging data, precise segmentation and analysis of medical images face unprecedented challenges. Addressing small sample sizes, significant variations, and structurally complex medical imaging data to improve the accuracy of early diagnosis has become a key issue in the medical field. This study proposes a Residual U-KAN model (ResU-KAN) to tackle this challenge and improve medical image segmentation accuracy. First, to address the model’s shortcomings in capturing long-distance dependencies and issues like potential gradient vanishing (or explosion) and overfitting, we introduce a Residual Convolution Attention (RCA) module. Second, to expand the model’s receptive field while performing multi-scale feature extraction, we introduce an Atrous Spatial Pyramid Pooling module (ASPP). Finally, experiments were conducted on three publicly available medical imaging datasets, and comparative analysis with existing state-of-the-art methods demonstrated the effectiveness of the proposed approach. Project page: https://github.com/Alfreda12/ResU-KAN

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Data Availability

https://github.com/Alfreda12/ResU-KAN/tree/main/inputs

Code Availability

https://github.com/Alfreda12/ResU-KAN

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Funding

This work was supported in part by Shijiazhuang Introducing High-level Talents’ Startup Funding Project (248790067A), the Startup Foundation for PhD of Hebei GEO University (No. BQ201322), Natural Science Foundation of Hebei Province (H2024403001), and Science Research Project Funding from Hebei Provincial Department of Education (BJK2024099).

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Author notes

  1. Haibin Wang and Zhenfeng Zhao contributed equally to this work.

Authors and Affiliations

  1. School of Information Engineering, Hebei GEO University, Shijiazhuang, Hebei, 050031, China

    Haibin Wang, Qi Liu & Shenwen Wang

  2. Hebei Huiji Technology Development Co., Ltd, Shijiazhuang, Hebei, 050000, China

    Zhenfeng Zhao

Authors
  1. Haibin Wang
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  2. Zhenfeng Zhao
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Contributions

Haibin Wang: Conceptualization, Funding Acquisition, Resources, Supervision, Writing - Review & Editing; Zhenfeng Zhao: Conceptualization, Investigation, Methodology, Data Curation, Visualization, Formal Analysis, Writing - Original Draft; Qi Liu: Resources, Supervision.

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Correspondence to Shenwen Wang.

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Wang, H., Zhao, Z., Liu, Q. et al. ResU-KAN: a medical image segmentation model integrating residual convolutional attention and atrous spatial pyramid pooling. Appl Intell 55, 568 (2025). https://doi.org/10.1007/s10489-025-06467-5

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  • DOI: https://doi.org/10.1007/s10489-025-06467-5

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