{"slug": "diffusion-models-in-medical-image-inpainting-challenges-solution-taxonomy-and", "title": "Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions", "summary": "A systematic review of 60 studies on diffusion models for medical image inpainting finds that denoising diffusion probabilistic models and latent diffusion models are the dominant architectures, primarily used for artifact removal, data augmentation, pseudo-healthy tissue reconstruction, and anomaly detection in MRI and CT imaging. The survey, published on arXiv, highlights strong performance in producing anatomically plausible reconstructions but notes challenges including a lack of standardized benchmarks, limited dataset diversity, and restricted validation across clinical applications.", "body_md": "arXiv:2607.21904v1 Announce Type: new\nAbstract: Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. In medical imaging, this task is particularly important because artifacts, missing information, and pathological alterations can compromise diagnostic reliability and downstream clinical applications. Recently, diffusion models have emerged as state-of-the-art generative approaches for medical image inpainting due to their ability to generate anatomically consistent reconstructions. This survey presents a systematic review of diffusion-based methods for medical image inpainting, covering the main architectures, applications, datasets, and evaluation strategies reported across 60 studies. In addition, we propose a taxonomy for diffusion-based approaches. The analysis reveals a rapid growth of research interest in diffusion-based medical image inpainting, with denoising diffusion probabilistic models and latent diffusion models emerging as the dominant architectures. The reviewed studies mainly focus on artifact removal, data augmentation, pseudo-healthy tissue reconstruction, and anomaly detection, particularly in magnetic resonance imaging and computed tomography imaging. Overall, diffusion models demonstrate strong performance in producing anatomically plausible reconstructions and aiding downstream clinical tasks. However, the review also highlights important challenges, including the lack of standardized benchmarks, limited dataset diversity, and restricted validation procedures across diverse clinical applications and imaging scenarios.", "url": "https://wpnews.pro/news/diffusion-models-in-medical-image-inpainting-challenges-solution-taxonomy-and", "canonical_source": "https://arxiv.org/abs/2607.21904", "published_at": "2026-07-27 04:00:00+00:00", "updated_at": "2026-07-27 04:27:53.396565+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "generative-ai", "computer-vision"], "entities": ["arXiv"], "alternates": {"html": "https://wpnews.pro/news/diffusion-models-in-medical-image-inpainting-challenges-solution-taxonomy-and", "markdown": "https://wpnews.pro/news/diffusion-models-in-medical-image-inpainting-challenges-solution-taxonomy-and.md", "text": "https://wpnews.pro/news/diffusion-models-in-medical-image-inpainting-challenges-solution-taxonomy-and.txt", "jsonld": "https://wpnews.pro/news/diffusion-models-in-medical-image-inpainting-challenges-solution-taxonomy-and.jsonld"}}