Background and Objectives

The multiple chest x-ray datasets released in the last years have ground-truth labels intended for different computer vision tasks, suggesting that performance in automated chest x-ray interpretation might improve by using a method that can exploit diverse types of annotations. This work presents a Deep Learning method based on the late fusion of different convolutional architectures, that allows training with heterogeneous data with a simple implementation, and evaluates its performance on independent test data. We focused on obtaining a clinically useful tool that could be successfully integrated into a hospital workflow.

Materials and Methods

Based on expert opinion, we selected four target chest x-ray findings, namely lung opacities, fractures, pneumothorax and pleural effusion. For each finding we defined the most suitable type of ground-truth label, and built four training datasets combining images from public chest x-ray datasets and our institutional archive. We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool. The performance was measured on two test datasets: an external openly-available dataset, and a retrospective institutional dataset, to estimate performance on the local population.

Results

The external and local test sets had 4376 and 1064 images, respectively, for which the model showed an area under the Receiver Operating Characteristics curve of 0.75 (95%CI: 0.74-0.76) and 0.87 (95%CI: 0.86-0.89) in the detection of abnormal chest x-rays. For the local population, a sensitivity of 86% (95%CI: 84-90), and a specificity of 88% (95%CI: 86-90) were obtained, with no significant differences between demographic subgroups. We present examples of heatmaps to show the accomplished level of interpretability, examining true and false positives.

Conclusion

This study presents a new approach for exploiting heterogeneous labels from different chest x-ray datasets, by choosing Deep Learning architectures according to the radiological characteristics of each pathological finding. We estimated the tool's performance on the local population, obtaining results comparable to state-of-the-art metrics. We believe this approach is closer to the actual reading process of chest x-rays by professionals, and therefore more likely to be successful in a real clinical setting.

中文翻译：

---

胸部X光自动分类：一种用于临床实施的符号学方法，通过四种深度学习架构的组合来利用不同类型的标签。

背景与目标

过去几年发布的多个胸部X射线数据集具有用于不同计算机视觉任务的地面标签，这表明通过使用可以利用各种类型注释的方法，自动胸部X射线解释的性能可能会提高。这项工作提出了一种基于不同卷积架构后期融合的深度学习方法，该方法允许通过简单的实现对异构数据进行训练，并评估其在独立测试数据上的性能。我们专注于获得可成功整合到医院工作流程中的临床有用工具。

材料和方法

根据专家的意见，我们选择了四个目标胸部X线检查结果，即肺部混浊，骨折，气胸和胸腔积液。对于每个发现，我们定义了最合适的地面真相标签类型，并构建了四个训练数据集，这些数据集结合了公共胸部X射线数据集和我们的机构档案中的图像。我们训练了四种不同的深度学习架构，并将其输出与后期融合策略结合在一起，从而获得了统一的工具。在两个测试数据集上对性能进行了测量：一个外部公开可用数据集和一个回顾性机构数据集，以估计本地人口的性能。

结果

外部和本地测试集分别具有4376和1064张图像，在该模型中，模型在接收器工作特征曲线下的面积分别为0.75（95％CI：0.74-0.76）和0.87（95％CI：0.86-0.89）。胸部X光检查异常。对于当地人群，灵敏度为86％（95％CI：84-90），特异性为88％（95％CI：86-90），人口统计学亚组之间无显着差异。我们提供了一些热图示例，以显示可实现的可解释性水平，检查真假肯定。

结论

这项研究提出了一种新方法，可通过根据每个病理发现的放射学特征选择深度学习架构，从不同的胸部X射线数据集中利用异构标签。我们估算了该工具在当地人群中的性能，获得了与最新指标相当的结果。我们认为这种方法更接近专业人士对胸部X光的实际读取过程，因此更有可能在实际临床环境中取得成功。