SKGC:胎儿先天性心脏病的一般语义层次知识引导分类框架
IEEE journal of biomedical and health informatics
|July 10, 2024
概括
一个新的深度学习框架,SKGC,使用最小的注释在胎儿超声图像中准确识别先天性心脏病 (CHD). 这种方法提高了胎儿心脏异常的诊断准确度,改进了现有的方法.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 先天性心脏病 (CHD) 是先天性残疾的主要原因.
- 目前用于诊断胎儿心脏病的深度学习方法在胎儿超声图像中有局限性,包括依赖全球特征或广泛的像素级注释.
- 基于训练细分模型的像素级注释通常是昂贵的,而且无法使用.
研究的目的:
- 提出SKGC,一个新的框架来诊断正常或异常的四腔心 (4CH) 图像从胎儿超声波.
- 为了提高CHD的诊断准确性,使用有限数量的注释口罩.
- 开发一种利用解剖结构的语义知识的方法.
主要方法:
- SKGC框架包括一个语义层面的知识提取模块 (SKEM),一个多知识融合模块 (MFM) 和一个分类模块 (CM).
- SKEM提取了高级语义知识,代表了解剖结构.
- MFM将语义知识与原始图像数据融合;一个新的损失函数增强了前景-后台预测约束.
主要成果:
- 在两个数据集上,SKGC实现了高精度:NA-4CH的99.68%,FEST的95.40%.
- 该框架显示了精度的显著改善,仅用10个标记的口罩,精度从74.68%增加到88.14%.
- 拟议的损失函数提高了语义层面知识提取的质量.
结论:
- SKGC为胎儿4CH超声波图像中CHD识别提供了通用和准确的框架.
- 该方法有效地利用有限的注释,克服了广泛的像素级数据要求的挑战.
- 在临床实践中,SKGC显示了改善先天性心脏病诊断的巨大潜力.
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