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

arXiv: https://arxiv.org/abs/2502.16771 · HTML: https://mlsanity.com/paper/2502.16771/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 8 of 8)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting](https://mlsanity.com/paper/2403.14499/) (2024)
- [Advancing Brain Tumor Inpainting with Generative Models](https://mlsanity.com/paper/2402.01509/) (2024)

## Built on by (most cited first, 3 of 3)

- [Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions](https://mlsanity.com/paper/2607.21904/) (2026)
- [Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty](https://mlsanity.com/paper/2608.13223/) (2026)
- [SurgSkill-Bench: A Benchmark for Multimodal Surgical Skill Assessment](https://mlsanity.com/paper/2608.30872/) (2026)

## Abstract (arXiv)

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.