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

2026-07-27 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Medical Imaging AI · Depth: Expert, extended

Summary

A systematic review of 60 studies published between 2023 and 2026 analyzes diffusion-based methods for medical image inpainting, a critical task for reconstructing missing or corrupted regions. The survey, following the PRISMA framework, identifies denoising diffusion probabilistic models (DDPMs) and latent diffusion models (LDMs) as dominant architectures, accounting for 56.7% and 33.3% of reviewed works, respectively. Key applications include artifact removal, data augmentation, pseudo-healthy tissue reconstruction, and anomaly detection, primarily in magnetic resonance imaging (MRI) and computed tomography (CT). While diffusion models show strong performance in generating anatomically plausible reconstructions, the review highlights challenges such as a lack of standardized benchmarks, limited dataset diversity, and restricted validation across diverse clinical applications. A case study benchmarked ten models, finding LDM-based NeuroLIT and DDPM-based DM-Inpainting and TPDM as top performers.

Key takeaway

For AI Scientists and Machine Learning Engineers developing medical imaging solutions, you should prioritize evaluating latent diffusion models (LDMs) for inpainting tasks, especially where computational efficiency and high reconstruction fidelity are critical. While DDPMs are prevalent, LDM-based approaches like NeuroLIT demonstrated superior performance in benchmarks. You must also advocate for standardized benchmarks and diverse datasets to ensure your models generalize robustly across clinical scenarios, moving beyond single-organ or single-pathology validations.

Key insights

Diffusion models excel in medical image inpainting, providing stable training and anatomically consistent reconstructions despite current validation challenges.

Principles

• Diffusion models avoid GAN's training instabilities and mode collapse.
• LDMs reduce computational cost by operating in a lower-dimensional latent space.
• U-Net backbones provide strong inductive bias for local spatial structures.

Method

A systematic literature search across Scopus, Web of Science, and IEEE Xplore databases, following the PRISMA framework, identified 60 studies published from 2023 to 2026 for analysis.

In practice

• Prioritize LDM-based methods like NeuroLIT for high reconstruction quality and efficiency.
• Implement DDPMs for robust artifact removal across various imaging modalities.
• Utilize diffusion models for data augmentation to address class imbalance in datasets.

Topics

• Diffusion Models
• Medical Image Inpainting
• Latent Diffusion Models
• Denoising Diffusion Probabilistic Models
• Artifact Removal
• Data Augmentation

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.