对基于医学图像的诊断的扩散模型进行系统审查:方法,分类学,临床整合,可解释性和未来方向
Mohammad Azad1, Nur Mohammad Fahad2,3, Mohaimenul Azam Khan Raiaan2,4
1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
扩散模型在用于诊断的医学成像中显示出前景. 需要进一步的研究来改善可解释性,临床医生集成和临床使用的实时性能.
科学领域:
- * 人工智能 * 人工智能
- * 计算机视觉 计算机视觉
- * 医疗信息学 医疗信息学
背景情况:
- *扩散模型是高级生成模型,对于高分辨率图像合成和重建至关重要.
- *它们的应用正在快速扩展到计算机视觉和健康信息学领域,特别是在医学成像领域.
- * 缺乏临床成像中扩散模型的结构化概述,这给研究人员带来了挑战.
研究的目的:
- *系统地审查扩散模型在医疗成像中用于诊断目的的应用.
- * 提供扩散模型原则,应用和该领域的局限性的综合概述.
- * 确定临床翻译的未来研究方向.
主要方法:
- * 根据PRISMA 2020指南 (2013-2024) 进行系统审查.
- *包括使用扩散模型进行医学诊断任务的同行评审研究.
- * 由于研究异质性而导致叙事综合方法;偏差风险评估.
主要成果:
- *包括68项研究,涵盖异常检测,分类,消除异常,生成,重建,细分,超分辨率和图像对图像翻译.
- *可解释的人工智能 (22.06%),临床医生的参与 (57.35%) 和实时实施 (10.30%) 被注意到.
- * 结果表明有很强的诊断潜力,但强调了报告的变化,方法上的不一致性和有限的现实世界验证.
结论:
- *扩散模型对诊断成像具有显著的前景.
- *临床部署需要在可解释性,临床医生集成和实时能力方面取得进展.
- *确定了12个关键研究方向,以指导未来的开发和临床转化.
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