  Chest radiography is one of the most common types of diagnostic radiology
exams, which is critical for screening and diagnosis of many different thoracic
diseases. Specialized algorithms have been developed to detect several specific
pathologies such as lung nodule or lung cancer. However, accurately detecting
the presence of multiple diseases from chest X-rays (CXRs) is still a
challenging task. This paper presents a supervised multi-label classification
framework based on deep convolutional neural networks (CNNs) for predicting the
risk of 14 common thoracic diseases. We tackle this problem by training
state-of-the-art CNNs that exploit dependencies among abnormality labels. We
also propose to use the label smoothing technique for a better handling of
uncertain samples, which occupy a significant portion of almost every CXR
dataset. Our model is trained on over 200,000 CXRs of the recently released
CheXpert dataset and achieves a mean area under the curve (AUC) of 0.940 in
predicting 5 selected pathologies from the validation set. This is the highest
AUC score yet reported to date. The proposed method is also evaluated on the
independent test set of the CheXpert competition, which is composed of 500 CXR
studies annotated by a panel of 5 experienced radiologists. The performance is
on average better than 2.6 out of 3 other individual radiologists with a mean
AUC of 0.930, which ranks first on the CheXpert leaderboard at the time of
writing this paper.
