Diffusion models advance medical image inpainting, survey finds

A recent survey paper published on arXiv details the advancements and challenges in using diffusion models for medical image inpainting. The paper systematically reviews 60 studies, highlighting the growing research interest and the effectiveness of models like denoising diffusion probabilistic models and latent diffusion models in reconstructing missing or corrupted medical image data. While these models show strong performance in applications such as artifact removal and data augmentation, particularly in MRI and CT scans, the survey also identifies critical challenges including the absence of standardized benchmarks, limited dataset diversity, and insufficient validation across various clinical scenarios. AI

IMPACT Highlights the potential of diffusion models to improve diagnostic accuracy and clinical applications in medical imaging.

RANK_REASON Survey paper analyzing a specific application of generative models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• CT
• denoising diffusion probabilistic models
• Diffusion Models
• latent diffusion models
• Medical Image Inpainting

• paper
• model release

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