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U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

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arxiv 2406.02918 v3 pith:ADWJY34T submitted 2024-06-05 eess.IV cs.CV

Chenxin Li , Xinyu Liu , Wuyang Li , Cheng Wang , Hengyu Liu , Yifan Liu , Zhen Chen , Yixuan Yuan This is my paper · ORCID
classification eess.IVcs.CV
keywords u-kanimagesegmentationmedicalu-netaccuracybackbonediffusion
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U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models. While numerous innovative designs and improvements have been introduced by incorporating transformers or MLPs, the networks are still limited to linearly modeling patterns as well as the deficient interpretability. To address these challenges, our intuition is inspired by the impressive results of the Kolmogorov-Arnold Networks (KANs) in terms of accuracy and interpretability, which reshape the neural network learning via the stack of non-linear learnable activation functions derived from the Kolmogorov-Anold representation theorem. Specifically, in this paper, we explore the untapped potential of KANs in improving backbones for vision tasks. We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of U-KAN by higher accuracy even with less computation cost. We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures. These endeavours unveil valuable insights and sheds light on the prospect that with U-KAN, you can make strong backbone for medical image segmentation and generation. Project page:\url{https://yes-u-kan.github.io/}.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation

    cs.AR 2025-12 conditional novelty 7.0 of 10

    Quantized, pruned Kolmogorov-Arnold Networks can be compiled directly into FPGA lookup tables, achieving extreme latency/resource reductions and matching state-of-the-art LUT-based networks on several benchmarks.

  2. KAN-SAs: Efficient Acceleration of Kolmogorov-Arnold Networks on Systolic Arrays

    cs.AR 2025-11 conditional novelty 7.0 of 10

    A systolic-array accelerator that tabulates B-splines and exploits B-spline local support achieves ~100% PE utilization and a 2x cycle reduction for KAN inference compared with a conventional systolic array.

  3. MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy

    cs.CV 2025-08 unverdicted novelty 6.0 of 10

    MetaScope, an optics-driven network, corrects metalens endoscope images and outperforms prior methods on segmentation and restoration.

  4. Improving Memory Efficiency for Training KANs via Meta Learning

    cs.LG 2025-06 conditional novelty 6.0 of 10

    MetaKANs generates each KAN activation function from a shared prompt-conditioned meta-learner, cutting trainable parameters toward MLP level while retaining comparable or better accuracy on tested benchmarks.

  5. Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

    quant-ph 2025-09 reject novelty 5.0 of 10

    QKANs show strong empirical performance on regression, vision, and language tasks, but the claimed exponential parameter reduction is not rigorously established.

  6. Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline

    cs.CV 2025-06 conditional novelty 5.0 of 10

    TAO pipelines object-centric anomaly scores into SAM2 prompts with a temporal consistency filter to obtain pixel-level anomaly segmentation and tracking.

  7. "KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Placing a KAN layer between two linear layers improves spoken language understanding accuracy over linear-only baselines on several speech-intent datasets.

  8. FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks

    cs.CV 2025-07 reject novelty 4.0 of 10

    FORTRESS combines depthwise separable convolutions and a gated Kolmogorov-Arnold module to report F1 of 0.771 and mIoU of 0.677 on the CSDD benchmark, but the core KAN contribution is not isolated by ablation.

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