Deep learning techniques have been increasingly used to provide more accurate and more accessible diagnosis of thorax diseases on chest radiographs. However, due to the lack of dense annotation of large-scale chest radiograph data, this computer-aided diagnosis task is intrinsically a weakly supervised learning problem and remains challenging. In this paper, we propose a novel deep convolutional neural network called Thorax-Net to diagnose 14 thorax diseases using chest radiography. Thorax-Net consists of a classification branch and an attention branch. The classification branch serves as a uniform feature extraction–classification network to free users from the troublesome hand-crafted feature extraction and classifier construction. The attention branch exploits the correlation between class labels and the locations of pathological abnormalities via analyzing the feature maps learned by the classification branch. Feeding a chest radiograph to the trained Thorax-Net, a diagnosis is obtained by averaging and binarizing the outputs of two branches. The proposed Thorax-Net model has been evaluated against three state-of-the-art deep learning models using the patientwise official split of the ChestX-ray14 dataset and against other five deep learning models using the imagewise random data split. Our results show that Thorax-Net achieves an average per-class area under the receiver operating characteristic curve (AUC) of 0.7876 and 0.896 in both experiments, respectively, which are higher than the AUC values obtained by other deep models when they were all trained with no external data.

中文翻译：

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胸网：在胸片上对胸病分类的注意正则化深度神经网络

深度学习技术已被越来越多地用于在胸部X光片上提供更准确，更易于诊断的胸部疾病。但是，由于缺乏对大型胸部X射线照片数据的密集注释，因此这种计算机辅助的诊断任务本质上是一个弱监督的学习问题，并且仍然具有挑战性。在本文中，我们提出了一种新颖的深层卷积神经网络，称为Thorax-Net，可以使用胸部X射线照相术诊断14种胸部疾病。Thorax-Net由分类分支和注意分支组成。分类分支用作统一的特征提取-分类网络，使用户摆脱麻烦的手工特征提取和分类器构造。注意分支通过分析由分类分支学习的特征图来利用类别标签和病理异常位置之间的相关性。将胸部X光片输入经过训练的胸网，可以通过对两个分支的输出求平均并进行二值化来获得诊断。拟议的Thorax-Net模型已针对使用ChestX-ray14数据集的患者方面官方划分的三个最新的深度学习模型进行了评估，并使用图像方面的随机数据进行了针对其他五个深度学习模型的评估。我们的结果表明，在两个实验中，Thorax-Net在接收器工作特性曲线（AUC）下分别达到0.7876和0.896的平均每类面积，