mlsanityData to 14 Oct 2026

Advancing Brain Tumor Inpainting with Generative Models

arXiv 2402.01509 ↗First posted 2 Feb 2024eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

Synthesizing healthy brain scans from diseased brain scans offers a potential solution to address the limitations of general-purpose algorithms, such as tissue segmentation and brain extraction algorithms, which may not effectively handle diseased images. We consider this a 3D inpainting task and investigate the adaptation of 2D inpainting methods to meet the requirements of 3D magnetic resonance imaging(MRI) data. Our contributions encompass potential modifications tailored to MRI-specific needs, and we conducted evaluations of multiple inpainting techniques using the BraTS2023 Inpainting datasets to assess their efficacy and limitations.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 2 Feb 2024.

Builds on

6 of 8 references.

References · 6

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021Palette: Image-to-Image Diffusion Modelscited by 1,300
  4. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  5. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 132
  6. 2021MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluationcited by 88

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  2. 2024Brain Tumour Removing and Missing Modality Generation using 3D WDMcited by 4
  3. 2025DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpaintingcited by 3
  4. 2025Post-Processing Methods for Improving Accuracy in MRI Inpaintingcited by 1