论文标题
从放射学报告中对多标签提取的人均注意
Paying Per-label Attention for Multi-label Extraction from Radiology Reports
论文作者
论文摘要
培训医学图像分析模型需要大量的专业注释数据，这些数据耗时且获得昂贵。图像通常伴随着自由文本放射学报告，这些报告是丰富的信息来源。在本文中，我们使用深度学习来解决从头部CT报告中自动提取结构化标签，以对可疑的中风患者进行成像。首先，我们提出了一组31个标签，对应于射线照相发现（例如高密度）和临床印象（例如出血）与神经系统异常有关。其次，受到以前的工作的启发，我们使用依赖标签的注意机制扩展了现有的最新神经网络模型。使用这种机制和简单的合成数据增强，我们能够根据放射科医生的报告（正，不确定，负面）对单个模型进行鲁棒提取许多标签。该方法可以在进一步的研究中使用，以有效地从医学文本中提取许多标签。
Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this paper, we tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients, using deep learning. Firstly, we propose a set of 31 labels which correspond to radiographic findings (e.g. hyperdensity) and clinical impressions (e.g. haemorrhage) related to neurological abnormalities. Secondly, inspired by previous work, we extend existing state-of-the-art neural network models with a label-dependent attention mechanism. Using this mechanism and simple synthetic data augmentation, we are able to robustly extract many labels with a single model, classified according to the radiologist's reporting (positive, uncertain, negative). This approach can be used in further research to effectively extract many labels from medical text.
