自动胸部疾病诊断使用多分支残留注意网络
Dongfang Li1, Hua Huo2, Shupei Jiao1
1School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, Henan, China.
Scientific reports
|May 24, 2024
概括
一个新的深度学习模型,MBRANet,通过整合多尺度特征和解决不平衡的数据集,通过胸部X射线增强胸部疾病诊断. 这种先进的计算机辅助诊断 (CAD) 系统在基准数据集上表现得更好.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 深度学习用于医学诊断
背景情况:
- 胸部X射线 (CXR) 对于诊断胸部疾病至关重要,但当前的计算机辅助诊断 (CAD) 方法在多尺度特征集成和不平衡数据集方面扎.
- 现有方法的局限性需要对胸部疾病的CAD取得进展,以提高诊断准确性和效率.
研究的目的:
- 提出一个新的多分支机构残留注意网络 (MBRANet) 以改进使用CXR图像进行胸部疾病诊断.
- 解决基于CXR的CAD系统中多尺度特征提取和数据不平衡的挑战.
主要方法:
- 开发了MBRANet,采用了新的残余结构和一个坐标注意 (CA) 模块来进行多级特征提取.
- 由特征金字塔网络 (FPN) 和具有类特定剩余注意力的多分支特征分类器 (MFC) 启发实施的多规模特征融合.
- 使用BCEWithLabelSmoothing损失函数来增强概括并减轻类不平衡.
主要成果:
- 在基准数据集上,MBRANet实现了高平均AUC:胸部X-Ray14 (0.841),CheXpert (0.895),MIMIC-CXR (0.805) 和IU X-Ray (0.745).
- 拟议的方法在这些数据集中显示了与最先进的基线相比的优异性能.
- 新型组件有效地解决了功能集成和数据集不平衡方面的局限性.
结论:
- MBRANet代表了对胸部疾病的计算机辅助诊断的重大进步,提供了更高的准确性和稳定性.
- 多尺度特征,注意力机制和高级损失功能的集成为分析胸部X射线提供了强大的工具.
- 这种方法有望增强放射学中的临床决策.
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