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在胸部X射线中以解剖学为指导的弱监督的异常定位

Ke Yu¹, Shantanu Ghosh¹, Zhexiong Liu¹

¹University of Pittsburgh, Pittsburgh, PA, USA.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|July 2, 2024

概括

本研究介绍了AGXNet,这是一种用于医学图像分析的新框架,利用放射学报告信息来改善异常检测. AGXNet有效地在胸部X射线中定位疾病和解剖异常,提高诊断准确度.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

创建大型注释医疗图像数据集以检测异常是资源密集的.
放射学报告的弱监督提供了一个潜在的解决方案,但往往忽略了解剖细节.
现有的方法由于稀疏和模两可,与来自自然语言处理 (NLP) 的杂标签作斗争.

研究的目的:

开发一个解剖学导向的胸部X射线网络 (AGXNet),以改善医学图像分析中的弱注释.
从放射学报告中利用病理学观察和解剖学提及.
解决NLP挖掘的弱标签中的标签噪音和稀疏性.

主要方法:

提出了一种级联式的两网框架 (AGXNet) 用于识别解剖异常和病理观察.
引入了解剖学引导的注意模块,以将观察网络集中在相关的解剖学区域上.
员工正面未标记 (PU) 学习处理没有提及并不意味着负面标签的情况.

主要成果:

在MIMIC-CXR数据集上,AGXNet在局部化疾病和解剖异常方面表现出有效性.
从AGXNet学习的特征表示可以转移到NIH胸部X射线数据集.
在疾病分类方面取得了最先进的表现,在疾病局部化方面取得了竞争性结果.

结论:

通过整合解剖学上下文,AGXNet成功地解决了医学成像中弱注释的局限性.
拟议的方法增强了放射学报告的实用性,用于训练强大的异常检测模型.
在医疗图像分析中,AGXNet显示出提高诊断准确性和效率的前景.

关键词:

班级激活地图班级激活地图疾病检测检测疾病检测 PU学习PU学习PU学习剩余的注意力注意力软弱监督的学习学习.

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