论文标题
多疾病胸部X射线诊断的广义深度学习模型
A generalized deep learning model for multi-disease Chest X-Ray diagnostics
论文作者
论文摘要
我们研究了深卷积神经网络（CNN）对从多个部位收集的胸部X射线分类的任务的普遍性。我们使用来自不同患者人群的三个独立地点的数据集系统地训练该模型：国家卫生研究院（NIH），斯坦福大学医学中心（CHEXPERT）和SHIFA国际医院（SIH）。我们制定了一种顺序训练方法，并证明该模型使用三个站点的测试集产生广义的预测性能。我们的模型在多个数据集中训练时会更好地概括，而CHEXPERT-SHIFA-NET模型的性能要好得多（P值<0.05）比在单个数据集中训练的模型中的4种不同的疾病类别中的3个模型。培训的代码将在发行时提供开源：www.github.com/link-to-code。
We investigate the generalizability of deep convolutional neural network (CNN) on the task of disease classification from chest x-rays collected over multiple sites. We systematically train the model using datasets from three independent sites with different patient populations: National Institute of Health (NIH), Stanford University Medical Centre (CheXpert), and Shifa International Hospital (SIH). We formulate a sequential training approach and demonstrate that the model produces generalized prediction performance using held out test sets from the three sites. Our model generalizes better when trained on multiple datasets, with the CheXpert-Shifa-NET model performing significantly better (p-values < 0.05) than the models trained on individual datasets for 3 out of the 4 distinct disease classes. The code for training the model will be made available open source at: www.github.com/link-to-code at the time of publication.
