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English(EN) Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions

扩散模型推动医学图像修复进展，一项调查发现

作者 PulseAugur 编辑部 · [2 个来源] ·

一篇最近发表在arXiv上的调查论文详细介绍了使用扩散模型进行医学图像修复的进展和挑战。该论文系统地回顾了60项研究，强调了研究兴趣的增长以及像去噪扩散概率模型和潜在扩散模型这类模型在重建缺失或损坏的医学图像数据方面的有效性。虽然这些模型在伪影去除和数据增强等应用中表现出强大的性能，尤其是在MRI和CT扫描中，但该调查也指出了关键挑战，包括缺乏标准化基准、数据集多样性有限以及在各种临床场景中的验证不足。 AI

影响 强调了扩散模型在提高医学影像诊断准确性和临床应用方面的潜力。

排序理由 分析生成模型特定应用的调查论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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扩散模型推动医学图像修复进展，一项调查发现

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Arthur Dantas Mangussi, Joana Cristo Santos, Ricardo Cardoso Pereira, Ana Carolina Lorena, M\'ario A. T. Figueiredo, Pedro Henriques Abreu ·

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

    arXiv:2607.21904v1 Announce Type: cross 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, missin…

  2. arXiv cs.CV TIER_1 English(EN) · Jiaqi Kuang, Zihao Guo, Zhongmin Qian ·

    图像修复中的随机动力学

    arXiv:2607.24140v1 Announce Type: new Abstract: Image inpainting aims to recover missing regions while preserving structural consistency. We propose a non-parametric method without network training based on data-guided stochastic dynamics. Starting from a masked image, the missin…

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