用KAN网络提升脑肿瘤图像修复质量，生成更真实细节。
DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting
- 将KAN网络融入扩散模型，更好捕捉脑影像非线性特征。
- 在BraTS数据集上重建细节更丰富，边缘更平滑，优于现有方法。
- 适合医学图像修复研究者，尤其关注高保真重建的场景。
脑肿瘤会延迟标准预处理流程，影响后续检查。脑部图像修复为肿瘤组织处理提供可行但困难的解决方案，有助于提升诊断与治疗精度。然而，大多数基于U-Net的生成模型难以捕捉脑影像中复杂的非线性潜在表征。为此，本文提出DiffKAN-Inpainting，一种融合扩散模型与柯尔莫哥洛夫-阿诺德网络（Kolmogorov-Arnold Networks, KAN）架构的新方法。在去噪过程中，引入RePaint方法与肿瘤信息，生成更高保真度且边缘更平滑的图像。定性与定量结果表明，相较于当前最优方法，该模型在BraTS数据集上实现了更细致、更真实的健康脑组织重建。消融实验所得知识为未来研究在性能与计算成本间平衡提供了参考。
原文摘要 · Abstract (English)
Brain tumors delay the standard preprocessing workflow for further examination. Brain inpainting offers a viable, although difficult, solution for tumor tissue processing, which is necessary to improve the precision of the diagnosis and treatment. Most conventional U-Net-based generative models, however, often face challenges in capturing the complex, nonlinear latent representations inherent in brain imaging. In order to accomplish high-quality healthy brain tissue reconstruction, this work proposes DiffKAN-Inpainting, an innovative method that blends diffusion models with the Kolmogorov-Arnold Networks architecture. During the denoising process, we introduce the RePaint method and tumor information to generate images with a higher fidelity and smoother margin. Both qualitative and quantitative results demonstrate that as compared to the state-of-the-art methods, our proposed DiffKAN-Inpainting inpaints more detailed and realistic reconstructions on the BraTS dataset. The knowledge gained from ablation study provide insights for future research to balance performance with computing cost.
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