DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting

• URL: http://arxiv.org/abs/2502.16771v1
• Date: Mon, 24 Feb 2025 01:34:28 GMT
• Title: DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting
• Abstract summary: Brain inpainting offers a viable, although difficult, solution for tumor tissue processing.<n>This work proposes DiffKAN-Inpainting, an innovative method that blends diffusion models with the Kolmogorov-Arnold Networks architecture.<n>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.
• Score: 11.9146658587331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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