The MIMIC-CXR dataset is (to date) the largest publicly released chest x-ray dataset consisting of 473,064 chest x-rays and 206,574 radiology reports collected from 63,478 patients. We present the results of training and evaluating a collection of deep convolutional neural networks on this dataset to recognize multiple common thorax diseases. To the best of our knowledge, this is the first work that trains CNNs for this task on such a large collection of chest x-ray images, which is over four times the size of the largest previously released chest x-ray corpus. We describe and evaluate individual CNN models trained on frontal and lateral CXR view types. In addition, we present a novel DualNet architecture that emulates routine clinical practice by simultaneously processing both frontal and lateral CXR images obtained from a radiological exam. Our DualNet architecture shows improved performance in recognizing findings in CXR images when compared to applying separate baseline frontal and lateral classifiers.

本文介绍了使用MIMIC-CXR数据集训练并评估深度卷积神经网络来识别多种常见胸部疾病的结果，提出了一种可以同时处理前后位胸透图像的新型DualNet体系结构，并通过与使用单独基准前位和侧位分类器进行比较来展示DualNet体系结构的性能提升。

基于双重卷积神经网络的大规模自动化前后胸部 X 光读取