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

2025-02-24

AI Summary

Brain tumors severely disrupt standard preprocessing pipelines, creating an urgent yet challenging need for robust brain tissue inpainting. To address this, we propose the first diffusion-based brain tissue restoration method integrating Kolmogorov–Arnold Networks (KANs) into medical image generation. Our approach innovatively incorporates KANs—capable of modeling complex nonlinear anatomical relationships—within a RePaint-style sampling framework, guided by tumor masks to condition the denoising process. This design preserves global anatomical continuity while markedly improving edge smoothness and textural fidelity. Evaluated on the BraTS dataset, our method surpasses current state-of-the-art approaches: it achieves significant gains in PSNR (+1.2 dB) and SSIM (+0.03), and qualitative assessments demonstrate more natural tissue transitions. Ablation studies confirm that KANs play a critical role in capturing the intricate nonlinear structure of brain MRI data.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Improves brain tumor inpainting precision
• Overcomes U-Net model limitations
• Enhances image reconstruction fidelity

Innovation

• Diffusion models integration
• Kolmogorov-Arnold Networks architecture
• RePaint method application