# Brain Tumour Removing and Missing Modality Generation using 3D WDM

arXiv: https://arxiv.org/abs/2411.04630 · HTML: https://mlsanity.com/paper/2411.04630/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 22 of 22)

- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [ResViT: Residual vision transformers for multi-modal medical image synthesis](https://mlsanity.com/paper/2106.16031/) (2021)
- [Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1802.01221/) (2018)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis](https://mlsanity.com/paper/2402.19043/) (2024)
- [Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation](https://mlsanity.com/paper/2211.03364/) (2022)
- [Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis](https://mlsanity.com/paper/2307.10094/) (2023)
- [Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing](https://mlsanity.com/paper/2303.15288/) (2023)
- [How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation](https://mlsanity.com/paper/2402.17317/) (2024)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)](https://mlsanity.com/paper/2305.09011/) (2023)
- [Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting](https://mlsanity.com/paper/2403.14499/) (2024)
- [Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling](https://mlsanity.com/paper/2111.04090/) (2021)
- [Denoising Diffusion Models for Inpainting of Healthy Brain Tissue](https://mlsanity.com/paper/2402.17307/) (2024)
- [BraSyn 2023 challenge: Missing MRI synthesis and the effect of different learning objectives](https://mlsanity.com/paper/2403.07800/) (2024)
- [Advancing Brain Tumor Inpainting with Generative Models](https://mlsanity.com/paper/2402.01509/) (2024)
- [DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality](https://mlsanity.com/paper/2312.00532/) (2023)

## Built on by (most cited first, 4 of 4)

- [Post-Processing Methods for Improving Accuracy in MRI Inpainting](https://mlsanity.com/paper/2510.15282/) (2025)
- [BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet](https://mlsanity.com/paper/2511.12853/) (2025)
- [POWDR: Pathology-preserving Outpainting with Wavelet Diffusion for 3D MRI](https://mlsanity.com/paper/2601.09044/) (2026)
- [Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting](https://mlsanity.com/paper/2607.27974/) (2026)

## Abstract (arXiv)

This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support clinical practice is increasing. However, many of these algorithms struggle with the presence of brain lesions or the absence of certain MRI modalities. The alterations in the brain's morphology leads to high variability and thus poor performance of predictive models that were trained only on healthy brains. The lack of information that is usually provided by some of the missing MRI modalities also reduces the reliability of the prediction models trained with all modalities. In order to improve the performance of these models, we propose the use of conditional 3D wavelet diffusion models. The wavelet transform enabled full-resolution image training and prediction on a GPU with 48 GB VRAM, without patching or downsampling, preserving all information for prediction. The code for these tasks is available at https://github.com/ShadowTwin41/BraTS_2023_2024_solutions.