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在医学成像中使用隐性扩散方法来进行视觉归因

Ammar Adeel Siddiqui¹, Santosh Tirunagari², Tehseen Zia³

¹Middlesex University, London, UK. AS3788@live.mdx.ac.uk.

Scientific reports

|January 6, 2025

概括

这项研究引入了一种用于医学成像的新视觉归因方法. 它使用生成人工智能通过比较异常图像与生成的正常图像来突出X射线中诊断相关的区域.

科学领域:

医学成像和人工智能医学成像和人工智能
计算机视觉在放射学中的应用.
可以解释的医疗保健人工智能

背景情况:

医疗成像的标准机器视觉通常可以检测疾病,但对于临床医生来说缺乏解释性.
视觉归属旨在识别诊断相关的图像组件,增强临床理解.
现有的方法可能无法完全弥合AI检测和临床解释性之间的差距.

研究的目的:

开发一种用于医学成像的新型生成视觉归因技术.
提高AI模型在临床放射学中的解释性和解释性.
为了进行比较分析,生成异常医学图像的正常对应物.

主要方法:

利用潜在扩散模型 (LDM) 与特定域的大型语言模型 (LLM) 相结合.
通过使用图像先验和条件化机制控制图像生成过程来生成正常的参考图像.
使用自然语言的文本提示,从医学科学和放射学中获得,用于条件生成模型.
使用异常和生成的正常图像之间的差异分析来创建归因图.

主要成果:

使用Frechet Inception Distance (FID),结构相似性 (SSIM) 和多尺度结构相似性 (MS-SSIM) 的指标对COVID-19放射数据库进行定量评估.
在生成异常胸部X射线的现实正常对应物方面表现出有效性.
开发的系统显示了潜在的能力,包括零射击局部疾病诱导,在cheXpert数据集上得到验证.

结论:

拟议的生成视觉归因技术有效地突出了医疗图像中的诊断相关区域.
整合LDM和LLM为可解释的医疗AI提供了一个强大的方法.
该方法有望通过为AI发现提供清晰的视觉解释来改善临床决策支持.

关键词:

扩散模型的扩散模型.可解释的人工智能医学成像医学成像视觉归属视觉归属是一种视觉归属.

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