Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantitative results to answer following research questions for the dataset: 1) What loss functions to use for training DCNN from scratch on ChestX-ray14 dataset that demonstrates high class imbalance and label co occurrence? 2) How to use cascading to model label dependency and to improve accuracy of the deep learning model?

本研究通过训练深度学习模型，提出了一种能够诊断14种疾病的级联深度神经网络，其表现优于基线并与其他已发布的方法竞争，探讨了针对ChestX-ray14数据集的训练DCNN的损失函数选取和使用级联建模标签依赖性以及提高深度学习模型准确性的问题。

胸部X光片的多标记分类的强化级联卷积神经网络