A hybrid Kolmogorov-Arnold network for medical image segmentation

• URL: http://arxiv.org/abs/2602.07702v1
• Date: Sat, 07 Feb 2026 21:00:38 GMT
• Title: A hybrid Kolmogorov-Arnold network for medical image segmentation
• Abstract summary: We propose U-KABS, a novel framework that integrates the expressive power of Kolmogorov-shaped Networks (KANs) with a U-grained encoder-decoder architecture to enhance segmentation performance.
• Score: 6.69087470775851
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical image segmentation plays a vital role in diagnosis and treatment planning, but remains challenging due to the inherent complexity and variability of medical images, especially in capturing non-linear relationships within the data. We propose U-KABS, a novel hybrid framework that integrates the expressive power of Kolmogorov-Arnold Networks (KANs) with a U-shaped encoder-decoder architecture to enhance segmentation performance. The U-KABS model combines the convolutional and squeeze-and-excitation stage, which enhances channel-wise feature representations, and the KAN Bernstein Spline (KABS) stage, which employs learnable activation functions based on Bernstein polynomials and B-splines. This hybrid design leverages the global smoothness of Bernstein polynomials and the local adaptability of B-splines, enabling the model to effectively capture both broad contextual trends and fine-grained patterns critical for delineating complex structures in medical images. Skip connections between encoder and decoder layers support effective multi-scale feature fusion and preserve spatial details. Evaluated across diverse medical imaging benchmark datasets, U-KABS demonstrates superior performance compared to strong baselines, particularly in segmenting complex anatomical structures.

Related papers

• AdaKAN: A dual-branch adaptive Kolmogorov-Arnold network for medical image segmentation [6.124773188525719]
  We propose AdaKAN, an Adaptive Kolmogorov-Arnold Network (KAN) that integrates convolutional operations with a novel efficient KAN block.<n>AdaKAN achieves state-of-the-art performance in segmentation accuracy.
  arXiv  Detail & Related papers  (2026-07-24T20:02:50Z)
• ConvNeXt-FD: A Fractal-Based Deep Model for Robust Biomedical Image Segmentation [0.0]
  This paper introduces ConvNeXt-FD, a novel deep learning architecture for robust biomedical image segmentation.<n>Our approach integrates a hybrid loss function combining the Dice coefficient with a boundary-aware regularization term.<n>We rigorously evaluate ConvNeXt-FD across six distinct biomedical datasets.
  arXiv  Detail & Related papers  (2026-05-21T04:58:24Z)
• Layout-Guided Controllable Pathology Image Generation with In-Context Diffusion Transformers [57.54843029965778]
  Controllable pathology image synthesis requires reliable regulation of spatial layout, tissue morphology, and semantic detail.<n>We propose In-Context Diffusion Transformer (IC-DiT), a layout-aware generative model that incorporates spatial layouts, textual descriptions, and visual embeddings into a unified diffusion transformer.<n>IC-DiT achieves higher fidelity, stronger spatial controllability, and better diagnostic consistency than existing methods.
  arXiv  Detail & Related papers  (2026-03-11T06:14:11Z)
• Data-Efficient Meningioma Segmentation via Implicit Spatiotemporal Mixing and Sim2Real Semantic Injection [6.992254817538211]
  We propose a novel dual-augmentation framework that integrates spatial manifold expansion and semantic object injection.<n>We show that our framework significantly enhances the data efficiency and robustness of state-of-the-art models, including nnU-Net and U-Mamba.
  arXiv  Detail & Related papers  (2026-01-19T09:11:28Z)
• TGC-Net: A Structure-Aware and Semantically-Aligned Framework for Text-Guided Medical Image Segmentation [56.09179939570486]
  We propose TGC-Net, a CLIP-based framework focusing on parameter-efficient, task-specific adaptations.<n>TGC-Net achieves state-of-the-art performance with substantially fewer trainable parameters, including notable Dice gains on challenging benchmarks.
  arXiv  Detail & Related papers  (2025-12-24T12:06:26Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• FunKAN: Functional Kolmogorov-Arnold Network for Medical Image Enhancement and Segmentation [0.0]
  Functional Kolmogorov-Arnold Network (FunKAN) is a novel interpretable neural framework for image processing.<n>FunKAN generalizes the Kolmogorov-Arnold representation theorem onto functional spaces and learns inner functions.<n>Our work bridges the gap between theoretical function approximation and medical image analysis, offering a robust, interpretable solution for clinical applications.
  arXiv  Detail & Related papers  (2025-09-16T20:13:48Z)
• Large Language Model Evaluated Stand-alone Attention-Assisted Graph Neural Network with Spatial and Structural Information Interaction for Precise Endoscopic Image Segmentation [16.773882069530426]
  We propose FOCUS-Med, which stands for Fusion of spatial and structural graph with attentional context-aware polyp segmentation.<n> FOCUS-Med integrates a Dual Graph Convolutional Network (Dual-GCN) module to capture contextual spatial and topological structural dependencies.<n>Experiments on public benchmarks demonstrate that FOCUS-Med achieves state-of-the-art performance across five key metrics.
  arXiv  Detail & Related papers  (2025-08-09T15:53:19Z)
• MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation [1.2721397985664153]
  We propose MS-UMamba, a novel hybrid convolutional-mamba model for fetal ultrasound image segmentation.<n>Specifically, we design a visual state space block integrated with a CNN branch, which leverages Mamba's global modeling strengths.<n>We also propose an efficient multi-scale feature fusion module, which integrates feature information from different layers.
  arXiv  Detail & Related papers  (2025-06-14T10:34:10Z)
• RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation [70.79072961974141]
  We propose RWKV-UNet, a novel model that integrates the RWKV structure into the U-Net architecture.<n>This integration enhances the model's ability to capture long-range dependencies and to improve contextual understanding.<n> Experiments on 11 benchmark datasets show that the RWKV-UNet achieves state-of-the-art performance on various types of medical image segmentation tasks.
  arXiv  Detail & Related papers  (2025-01-14T22:03:00Z)
• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data [10.774128925670183]
  This paper presents the Hybrid Early-fusion Attention Learning Network (HEALNet), a flexible multimodal fusion architecture. We conduct multimodal survival analysis on Whole Slide Images and Multi-omic data on four cancer datasets from The Cancer Genome Atlas (TCGA) HEALNet achieves state-of-the-art performance compared to other end-to-end trained fusion models.
  arXiv  Detail & Related papers  (2023-11-15T17:06:26Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.