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用于通过将X射线与临床数据融合来检测异常的MDF-Net

Chihcheng Hsieh¹, Isabel Blanco Nobre², Sandra Costa Sousa²

¹Queensland University of Technology, Brisbane, Australia.

Scientific reports

|September 23, 2023

概括

整合患者临床数据与胸部X射线显著提高深度学习 (DL) 模型的疾病局部化性能. 这种多模式的方法可以提高胸部成像分析的诊断准确性.

科学领域:

医学成像和人工智能医学成像和人工智能
放射学和诊断医学放射学和诊断医学
计算病理学计算病理学

背景情况:

目前用于胸部X射线分析的深度学习 (DL) 模型仅使用图像数据就能实现高性能.
放射科医生强调患者临床信息在准确诊断和图像解释中的关键作用.
整合各种数据模式是由于不同维度空间而面临的挑战.

研究的目的:

研究将患者临床数据纳入胸部X射线中疾病局部化DL分类器的性能的影响.
提出和评估一种能够处理临床和图像数据的新型多式联网DL架构.
提高胸部成像中自动疾病检测的准确性和可靠性.

主要方法:

开发了一种新的DL架构,采用两种融合方法,同时处理结构化临床数据和非结构化胸部X射线图像.
引入了一个空间化策略,以在Mask R-CNN框架内促进多模式学习,解决维度差异.
使用MIMIC-Eye数据集进行了广泛的实验,包括MIMIC-CXR,MIMIC IV-ED和REFLACX.

主要成果:

拟议的多式联络DL模型在疾病定位的平均精度上实现了12%的改进,与仅使用胸部X射线的标准面具R-CNN相比.
纳入患者临床数据显著提高了DL模型在疾病局部化任务中的性能.
废弃性研究证实了多式联接DL架构的关键贡献和临床数据的纳入.

结论:

综合患者临床数据和胸部X射线的多式DL架构为疾病定位提供了卓越的性能.
拟议的融合方法和空间化策略有效地实现了多模式学习,以提高诊断准确度.
这项研究强调了结合多种数据源的价值,以实现更强大,更可靠的AI驱动医学图像分析.

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