Department of Computer Science, Stanford University, Stanford, CA, USA.
Department of Radiology, Stanford University, Stanford, CA, USA.
Nat Commun. 2021 Mar 25;12(1):1880. doi: 10.1038/s41467-021-22018-1.
Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existing supervised machine learning systems. Leveraging recent advancements in natural language processing, we describe a weak supervision framework that extracts imperfect, yet highly granular, regional abnormality labels from free-text radiology reports. Our framework automatically labels each region in a custom ontology of anatomical regions, providing a structured profile of the pathologies in each imaging exam. Using these generated labels, we then train an attention-based, multi-task CNN architecture to detect and estimate the location of abnormalities in whole-body scans. We demonstrate empirically that our multi-task representation is critical for strong performance on rare abnormalities with limited training data. The representation also contributes to more accurate mortality prediction from imaging data, suggesting the potential utility of our framework beyond abnormality detection and location estimation.
计算决策支持系统可以在全身 FDG-PET/CT 工作流程中提供临床价值。然而，由于标记数据的有限可用性以及 PET/CT 成像检查的规模庞大，使得现有监督机器学习系统的应用具有挑战性。利用自然语言处理的最新进展，我们描述了一个弱监督框架，该框架可以从自由文本放射学报告中提取不完美但粒度非常细的区域性异常标签。我们的框架自动标记自定义解剖区域本体中的每个区域，为每个成像检查中的病理学提供结构化的概况。然后，我们使用这些生成的标签来训练基于注意力的多任务 CNN 架构，以检测和估计全身扫描中的异常位置。我们通过经验证明，我们的多任务表示对于具有有限训练数据的罕见异常的出色性能至关重要。该表示还有助于从成像数据中进行更准确的死亡率预测，这表明我们的框架除了异常检测和位置估计之外还有潜在的应用价值。
