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  • U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
    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
  • U-KAN Makes Strong Backbone for Medical Image Segmentation and . . .
    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
  • U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
    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
  • U-KAN makes strong backbone for medical image segmentation and . . .
    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
  • U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
    We empirically validate U-KAN on a wide range of medical segmentation benchmarks, achieving impressive accuracy and efficiency The application of U-KAN to existing diffusion models as an improved noise predictor demonstrates its potential in backboning generative tasks and broader vision settings
  • U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
    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
  • 论文精读-U-KAN Makes Strong Backbone for Medical . . .
    我们在多个医学图像分割任务下对该方法进行了实证评估。 此外，U-KAN 的适应性和有效性也凸显了其作为扩散模型中噪声预测的优越替代方案的潜力。 这些研究结果强调了探索 KAN 等非传统网络结构对于推进更广泛的视觉应用的重要性。
  • U-Net遇见KAN！U-KAN为医学图像分割和生成提供了强大 . . .
    具体而言，在本文中，我们探索了Kolmogorov-Anold网络（KAN）在提高视觉任务骨干方面尚未开发的潜力。 我们通过在标记化的中间表示（称为U-KAN）上集成专用的KAN层来研究、修改和重新设计已建立的U-Net pipeline。 严格的医学图像分割基准验证了U-KAN的优越性，即使计算成本较低，也能获得更高的精度。 我们进一步深入研究了U-KAN在扩散模型中作为一种替代U-Net噪声预测器的潜力，证明了它在生成面向任务的模型架构中的适用性。 这些努力揭示了宝贵的见解，并揭示了使用U-KAN，您可以为医学图像分割和生成提供强大的backbone的前景。





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