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首页> 外文期刊>JMIR Medical Informatics >Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers
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Assessment of the Robustness of Convolutional Neural Networks in Labeling Noise by Using Chest X-Ray Images From Multiple Centers

机译：通过使用多个中心的胸部X射线图像评估卷积神经网络在标记噪声中的鲁棒性

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Background Computer-aided diagnosis on chest x-ray images using deep learning is a widely studied modality in medicine. Many studies are based on public datasets, such as the National Institutes of Health (NIH) dataset and the Stanford CheXpert dataset. However, these datasets are preprocessed by classical natural language processing, which may cause a certain extent of label errors. Objective This study aimed to investigate the robustness of deep convolutional neural networks (CNNs) for binary classification of posteroanterior chest x-ray through random incorrect labeling. Methods We trained and validated the CNN architecture with different noise levels of labels in 3 datasets, namely, Asan Medical Center-Seoul National University Bundang Hospital (AMC-SNUBH), NIH, and CheXpert, and tested the models with each test set. Diseases of each chest x-ray in our dataset were confirmed by a thoracic radiologist using computed tomography (CT). Receiver operating characteristic (ROC) and area under the curve (AUC) were evaluated in each test. Randomly chosen chest x-rays of public datasets were evaluated by 3 physicians and 1 thoracic radiologist. Results In comparison with the public datasets of NIH and CheXpert, where AUCs did not significantly drop to 16%, the AUC of the AMC-SNUBH dataset significantly decreased from 2% label noise. Evaluation of the public datasets by 3 physicians and 1 thoracic radiologist showed an accuracy of 65%-80%. Conclusions The deep learning–based computer-aided diagnosis model is sensitive to label noise, and computer-aided diagnosis with inaccurate labels is not credible. Furthermore, open datasets such as NIH and CheXpert need to be distilled before being used for deep learning–based computer-aided diagnosis.
机译：背景技术使用深度学习胸部X射线图像的计算机辅助诊断是一种广泛研究的医学模式。许多研究基于公共数据集，例如国家健康研究院（NIH）数据集和斯坦福Chexpert DataSet。但是，这些数据集通过经典的自然语言处理预处理，这可能导致某种程度的标签错误。目的本研究旨在通过随机不正确的标记来研究深卷积神经网络（CNNS）对后胸X射线二进制分类的鲁棒性。我们培训并验证了3个数据集中标签的不同噪声水平的CNN架构，即ASAN医疗中心 - 首尔国立大学Bondang医院（AMC-SNUBH），NIH和CHEXPERT，并用每个测试集测试模型。我们数据集中的每个胸部X射线的疾病由使用计算机断层扫描（CT）确认胸部放射学家。每次测试中评估接收器操作特性（ROC）和曲线）下的区域和区域。随机选择的公共数据集胸部X射线由3个医生和1个胸放射科医师评估。结果与NIH和CHEXPERT的公共数据集相比，AUCS没有显着降至16％，AMC-SNUBH数据集的AUC从2％的标签噪声显着降低。将公共数据集评估3个医生和1个胸放射科学家的准确性为65％-80％。结论基于深度学习的计算机辅助诊断模型对标签噪声敏感，并且具有不准确标签的计算机辅助诊断是不可信的。此外，在使用基于深度学习的计算机辅助诊断之前，需要蒸馏出诸如NIH和Chexpert的开放数据集。

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