注意引导部分域适应用于自动肺炎诊断从胸部X射线图像的自动肺炎诊断
IEEE journal of biomedical and health informatics
|September 11, 2023
概括
本研究介绍了以注意引导的部分域适应 (AGPDA) 网络,通过解决无监督域适应中的空间和语义负转移挑战来改善各种胸部X射线数据集的深度神经网络性能.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 深度神经网络 (DNN) 在胸部X射线 (CXR) 分析方面表现出色,但由于域间异质性,其性能下降.
- 无监督域适应 (UDA) 方法试图弥合域差距,但面临着诸如空间和语义负面转移等挑战.
研究的目的:
- 提出一种新的基于分类的框架,即以注意力为导向的部分域适应 (AGPDA) 网络.
- 为了克服UDA中的空间和语义负转移问题,用于CXR分析.
主要方法:
- AGPDA网络具有区域关注歧视区块 (RADB),使用轻量级的区域智能多对手网络.
- 它还包括一个剩余特征重新校准块 (RFRB),采用类加权最大平均差异 (MMD) 损失.
主要成果:
- 针对8598例肺炎和7163例非肺炎病例的两个CXR数据集的实验表明了AGPDA的卓越性能.
- 与基线相比,AGPDA在部分传输任务上显著提高了准确度,灵敏度和F1得分4.35%,4.05%和1.78%.
结论:
- 拟议的AGPDA网络有效地解决了用于CXR分析的UDA负面转移挑战.
- AGPDA增强了DNN在异质医学成像领域的稳定性和通用性.
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