  The chest X-rays (CXRs) is one of the views most commonly ordered by
radiologists (NHS),which is critical for diagnosis of many different thoracic
diseases. Accurately detecting thepresence of multiple diseases from CXRs is
still a challenging task. We present a multi-labelclassification framework
based on deep convolutional neural networks (CNNs) for diagnos-ing the presence
of 14 common thoracic diseases and observations. Specifically, we trained
astrong set of CNNs that exploit dependencies among abnormality labels and used
the labelsmoothing regularization (LSR) for a better handling of uncertain
samples. Our deep net-works were trained on over 200,000 CXRs of the recently
released CheXpert dataset (Irvinandal., 2019) and the final model, which was an
ensemble of the best performing networks,achieved a mean area under the curve
(AUC) of 0.940 in predicting 5 selected pathologiesfrom the validation set. To
the best of our knowledge, this is the highest AUC score yetreported to date.
More importantly, the proposed method was also evaluated on an inde-pendent
test set of the CheXpert competition, containing 500 CXR studies annotated by
apanel of 5 experienced radiologists. The reported performance was on average
better than2.6 out of 3 other individual radiologists with a mean AUC of 0.930,
which had led to thecurrent state-of-the-art performance on the CheXpert test
set.
