The advancement of machine learning algorithms in medical image analysis requires the expansion of training datasets. A popular and cost-effective approach is automated annotation extraction from free-text medical reports, primarily due to the high costs associated with expert clinicians annotating chest X-ray images. However, it has been shown that the resulting datasets are susceptible to biases and shortcuts. Another strategy to increase the size of a dataset is crowdsourcing, a widely adopted practice in general computer vision with some success in medical image analysis. In a similar vein to crowdsourcing, we enhance two publicly available chest X-ray datasets by incorporating non-expert annotations. However, instead of using diagnostic labels, we annotate shortcuts in the form of tubes. We collect 3.5k chest drain annotations for CXR14, and 1k annotations for 4 different tube types in PadChest. We train a chest drain detector with the non-expert annotations that generalizes well to expert labels. Moreover, we compare our annotations to those provided by experts and show "moderate" to "almost perfect" agreement. Finally, we present a pathology agreement study to raise awareness about ground truth annotations. We make our annotations and code available.

翻译：机器学习算法在医学图像分析中的进步需要扩大训练数据集。一种流行且经济高效的方法是从自由文本医疗报告中自动提取标注，这主要是因为专家临床医生对胸部X光片进行标注的成本高昂。然而，研究表明，由此产生的数据集容易受到偏差和捷径学习的影响。另一种扩大数据集规模的策略是众包，这在通用计算机视觉领域已被广泛采用，并在医学图像分析中取得了一定成功。类似于众包的方式，我们通过纳入非专家标注来增强两个公开可用的胸部X光片数据集。但不同于使用诊断标签，我们以导管形式标注了捷径特征。我们为CXR14收集了3500个胸腔引流管标注，为PadChest收集了4种不同导管类型的1000个标注。我们利用这些非专家标注训练了一个胸腔引流管检测器，该检测器能够良好地泛化到专家标签。此外，我们将我们的标注与专家提供的标注进行了比较，显示出“中等”到“几乎完美”的一致性。最后，我们开展了一项病理一致性研究，以提高对金标准标注的认识。我们将标注数据和代码公开。