Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions

• URL: http://arxiv.org/abs/2607.21904v1
• Date: Fri, 24 Jul 2026 02:19:52 GMT
• Title: Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions
• Abstract summary: diffusion models have emerged as state-of-the-art generative approaches for medical image inpainting.<n>The reviewed studies mainly focus on artifact removal, data augmentation, pseudo-healthy tissue reconstruction, and anomaly detection.<n>However, the review also highlights important challenges, including the lack of standardized benchmarks, limited dataset diversity, and restricted validation procedures.
• Score: 4.084837084015297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection [58.92871907037662]
  Unsupervised medical anomaly detection learns normal anatomical patterns from healthy training images and identifies deviations at test time.<n>Reconstruction-based and diffusion-based methods commonly use the difference between an input image and its reconstruction as anomaly evidence.<n>We propose discriminative mask-guided diffusion (DMD), a medical anomaly detection framework that complements residual-based localization with reconstruction-shift discrimination.
  arXiv  Detail & Related papers  (2026-08-01T04:56:39Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Causal Disentanglement for Robust Long-tail Medical Image Generation [80.15257897500578]
  We propose a novel medical image generation framework, which generates independent pathological and structural features.<n>We leverage a diffusion model guided by pathological findings to model pathological features, enabling the generation of diverse counterfactual images.
  arXiv  Detail & Related papers  (2025-04-20T01:54:18Z)
• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation.<n>We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• Introducing Shape Prior Module in Diffusion Model for Medical Image Segmentation [7.7545714516743045]
  We propose an end-to-end framework called VerseDiff-UNet, which leverages the denoising diffusion probabilistic model (DDPM) Our approach integrates the diffusion model into a standard U-shaped architecture. We evaluate our method on a single dataset of spine images acquired through X-ray imaging.
  arXiv  Detail & Related papers  (2023-09-12T03:05:00Z)
• Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey [73.86861112002593]
  We present a comprehensive review of recent diffusion model-based methods on image restoration.<n>We classify and emphasize the innovative designs using diffusion models for both IR and blind/real-world IR.<n>We propose five potential and challenging directions for the future research of diffusion model-based IR.
  arXiv  Detail & Related papers  (2023-08-18T08:40:38Z)
• DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification [32.67098520984195]
  We propose the first diffusion-based model (named DiffMIC) to address general medical image classification. Our experimental results demonstrate that DiffMIC outperforms state-of-the-art methods by a significant margin.
  arXiv  Detail & Related papers  (2023-03-19T09:15:45Z)
• Diffusion Models for Medical Image Analysis: A Comprehensive Survey [7.272308924113656]
  Denoising diffusion models, a class of generative models, have garnered immense interest lately in various deep-learning problems. Diffusion models are widely appreciated for their strong mode coverage and quality of the generated samples. This survey intends to provide a comprehensive overview of diffusion models in the discipline of medical image analysis.
  arXiv  Detail & Related papers  (2022-11-14T23:50:52Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.