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HGT:一种基于GCN的分层变压器,用于使用计算机断层图像和文本进行多模态周围假肢关节感染诊断

Ruiyang Li¹, Fujun Yang², Xianjie Liu²

¹College of Electronics and Information Engineering, Sichuan University, Chengdu 610041, China.

Sensors (Basel, Switzerland)

|July 14, 2023

概括

一种新的深度学习方法使用CT扫描和患者数据准确诊断假肢关节感染 (PJI). 这种方法提高了临床医生的诊断准确性和效率.

科学领域:

医疗成像医学成像
人工智能的人工智能
整形外科手术整形外科手术

背景情况:

假肢关节感染 (PJI) 是一个严重的并发症,具有诊断挑战.
当前的诊断标准缺乏整合多式联络数据,如CT图像和数字文本数据.
电脑扫描图像中的噪音和数据量差异阻碍了统一的诊断方法.

研究的目的:

为PJI引入一种基于深度学习的多式联络诊断方法.
为了有效地合并CT图像和数值患者数据的特征.
为了提高PJI检测的诊断准确性和效率.

主要方法:

开发了一个深度学习模型,HGT,利用多式联络技术.
采用单向选择性注意力 (美国) 机制进行特征合并.
使用基于图形卷积网络 (GCN) 的特征融合网络进行数据集成.
在定制的多式联运PJI数据集上评估性能.

主要成果:

实现了91.4%的精度 (ACC) 和95.9%的曲线下面积 (AUC).
在ACC方面表现2.9%,在AUC方面表现2.2%,优于最近的多式联络方式.
通过可解释性评估,在损伤部位表现出强大的焦点和定位能力.
该模型的参数数量为68M.

结论:

拟议的HGT方法为PJI诊断提供了一个有前途的工具.
这种多模式的方法提高了临床实践中的诊断准确性和效率.
该模型的可解释性证实了其识别相关病理特征的能力.

关键词:

在CT成像中使用CT成像.深度学习的诊断方法图形卷积神经网络 (GCNs) 是一种神经网络.多式联络融合多式联络融合人造关节感染 (PJI)单向选择性注意力 (美国)

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