MedKamba: A Novel Approach Integrating State-Space Models and Fractional Kolmogorov–Arnold Networks for Medical Image Segmentation

Amit Shakya, Akanksha Yadav, Rupesh Kumar, Lalit Sharma
Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:1890-1902, 2026.

Abstract

Medical image segmentation plays a crucial role in healthcare, serving as a key step in disease diagnosis and treatment planning. Convolutional neural networks (CNNs) are limited by their restricted receptive fields, whereas Transformer-based models suffer from quadratic computational cost. Recent advances such as Mamba, a selective state-space model with linear complexity, and its vision-oriented variant, the Visual State Space (VSS) models, have shown strong ability to capture long-range dependencies efficiently. However, they still exhibit shortcomings in segmentation tasks, including loss of pixel-level structural information and inefficient channel utilization. To address this, we introduce VSSM-based Local Aware Channel Enhancement (LACE) block, which incorporates local enhancement and channel attention to better preserve spatial detail. To this end, we proposed MedKamba, a novel U-shaped segmentation approach that employs a hybrid encoder with CNNs and LACE blocks to effectively capture both local and global contextual information. While the U-Net backbone remains highly efficient, its traditional skip connections rely on simple scale-matched fusion, limiting cross-scale interaction. To overcome this, we redesign the skip connections using Fractional Kolmogorov–Arnold Networks (f-KANs) to generate channel-wise attention weights from features aggregated across multiple stages. Experiments on two benchmark datasets demonstrate that MedKamba consistently outperforms competing approaches and produces more visually accurate segmentation results.

Cite this Paper

BibTeX
@InProceedings{pmlr-v315-shakya26a, title = {MedKamba: A Novel Approach Integrating State-Space Models and Fractional Kolmogorov–Arnold Networks for Medical Image Segmentation}, author = {Shakya, Amit and Yadav, Akanksha and Kumar, Rupesh and Sharma, Lalit}, booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning}, pages = {1890--1902}, year = {2026}, editor = {Huo, Yuankai and Gao, Mingchen and Kuo, Chang-Fu and Jin, Yueming and Deng, Ruining}, volume = {315}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v315/main/assets/shakya26a/shakya26a.pdf}, url = {https://proceedings.mlr.press/v315/shakya26a.html}, abstract = {Medical image segmentation plays a crucial role in healthcare, serving as a key step in disease diagnosis and treatment planning. Convolutional neural networks (CNNs) are limited by their restricted receptive fields, whereas Transformer-based models suffer from quadratic computational cost. Recent advances such as Mamba, a selective state-space model with linear complexity, and its vision-oriented variant, the Visual State Space (VSS) models, have shown strong ability to capture long-range dependencies efficiently. However, they still exhibit shortcomings in segmentation tasks, including loss of pixel-level structural information and inefficient channel utilization. To address this, we introduce VSSM-based Local Aware Channel Enhancement (LACE) block, which incorporates local enhancement and channel attention to better preserve spatial detail. To this end, we proposed MedKamba, a novel U-shaped segmentation approach that employs a hybrid encoder with CNNs and LACE blocks to effectively capture both local and global contextual information. While the U-Net backbone remains highly efficient, its traditional skip connections rely on simple scale-matched fusion, limiting cross-scale interaction. To overcome this, we redesign the skip connections using Fractional Kolmogorov–Arnold Networks (f-KANs) to generate channel-wise attention weights from features aggregated across multiple stages. Experiments on two benchmark datasets demonstrate that MedKamba consistently outperforms competing approaches and produces more visually accurate segmentation results.} }
Endnote
%0 Conference Paper %T MedKamba: A Novel Approach Integrating State-Space Models and Fractional Kolmogorov–Arnold Networks for Medical Image Segmentation %A Amit Shakya %A Akanksha Yadav %A Rupesh Kumar %A Lalit Sharma %B Proceedings of The 9th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Yuankai Huo %E Mingchen Gao %E Chang-Fu Kuo %E Yueming Jin %E Ruining Deng %F pmlr-v315-shakya26a %I PMLR %P 1890--1902 %U https://proceedings.mlr.press/v315/shakya26a.html %V 315 %X Medical image segmentation plays a crucial role in healthcare, serving as a key step in disease diagnosis and treatment planning. Convolutional neural networks (CNNs) are limited by their restricted receptive fields, whereas Transformer-based models suffer from quadratic computational cost. Recent advances such as Mamba, a selective state-space model with linear complexity, and its vision-oriented variant, the Visual State Space (VSS) models, have shown strong ability to capture long-range dependencies efficiently. However, they still exhibit shortcomings in segmentation tasks, including loss of pixel-level structural information and inefficient channel utilization. To address this, we introduce VSSM-based Local Aware Channel Enhancement (LACE) block, which incorporates local enhancement and channel attention to better preserve spatial detail. To this end, we proposed MedKamba, a novel U-shaped segmentation approach that employs a hybrid encoder with CNNs and LACE blocks to effectively capture both local and global contextual information. While the U-Net backbone remains highly efficient, its traditional skip connections rely on simple scale-matched fusion, limiting cross-scale interaction. To overcome this, we redesign the skip connections using Fractional Kolmogorov–Arnold Networks (f-KANs) to generate channel-wise attention weights from features aggregated across multiple stages. Experiments on two benchmark datasets demonstrate that MedKamba consistently outperforms competing approaches and produces more visually accurate segmentation results.
APA
Shakya, A., Yadav, A., Kumar, R. & Sharma, L.. (2026). MedKamba: A Novel Approach Integrating State-Space Models and Fractional Kolmogorov–Arnold Networks for Medical Image Segmentation. Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 315:1890-1902 Available from https://proceedings.mlr.press/v315/shakya26a.html.

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