  Chest X-ray (CXR) is the most common X-ray examination performed in daily
clinical practice for the diagnosis of various heart and lung abnormalities.
The large amount of data to be read and reported, with 100+ studies per day for
a single radiologist, poses a challenge in maintaining consistently high
interpretation accuracy. In this work, we propose a method for the
classification of different abnormalities based on CXR scans of the human body.
The system is based on a novel multi-task deep learning architecture that in
addition to the abnormality classification, supports the segmentation of the
lungs and heart and classification of regions where the abnormality is located.
We demonstrate that by training these tasks concurrently, one can increase the
classification performance of the model. Experiments were performed on an
extensive collection of 297,541 chest X-ray images from 86,876 patients,
leading to a state-of-the-art performance level of 0.883 AUC on average for 12
different abnormalities. We also conducted a detailed performance analysis and
compared the accuracy of our system with 3 board-certified radiologists. In
this context, we highlight the high level of label noise inherent to this
problem. On a reduced subset containing only cases with high confidence
reference labels based on the consensus of the 3 radiologists, our system
reached an average AUC of 0.945.
