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罗米亚:一个框架,用于创建强大的医疗成像AI模型,用于胸部X射线图

Aditi Anand¹, Sarada Krithivasan¹, Kaushik Roy¹

¹School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States.

Frontiers in radiology

|January 23, 2024

概括

我们开发了RoMIA,这是一个框架,以提高医疗成像中人工智能 (AI) 模型的稳定性. 通过减少因噪音或多样化输入数据引起的错误分类,RoMIA提高了诊断准确性.

科学领域:

医学成像人工智能医学成像人工智能
医疗保健中的深度学习
放射学信息学信息学

背景情况:

深度神经网络 (DNN) 在医学成像中显示出潜力,但易受输入噪声和变化的影响.
这一漏洞对AI在临床环境中的广泛采用构成了重大挑战.
确保人工智能诊断工具的可靠性对于患者安全和有效的医疗保健至关重要.

研究的目的:

为了提高用于胸部X光学分类的深度神经网络 (DNN) 的稳定性.
引入一个框架,RoMIA,用于创建更可靠的医学成像AI模型.
为了减轻人工智能模型在面对现实世界的数据干扰时的性能退化.

主要方法:

开发了RoMIA (强大的医疗成像AI) 框架,其中包括三个关键步骤:增加噪声的培训,通过输入混合进行微调,以及基于DCT的无声化.
应用RoMIA来训练使用CheXpert数据集的六种胸部X光学分类模型.
在CheXphoto数据集上评估模型稳定性,其中包括自然和合成扰乱图像.

主要成果:

用RoMIA训练的模型在强大的准确性方面显示出3%-5%的改进.
在评估的模型中,错误分类的平均减少率为22.6%.
在扰乱条件下,RoMIA显著提高了AI模型在分类胸部X射线图中的可靠性.

结论:

RoMIA框架有效地提高了医疗成像任务的AI模型的稳定性.
罗米亚提供了一种实际的方法来应对临床AI应用中的输入变化的挑战.
实施RoMIA可以通过增加信任和可靠性,促进AI在医学成像中的更广泛采用.

关键词:

人工智能的人工智能是人工智能.人工神经网络的人工神经网络胸部X射线图片胸部X射线图片医学成像医学成像放射学放射学是指放射学强度坚固性坚固性

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