  Chest radiography is the most common radiographic examination performed in
daily clinical practice for the detection of various heart and lung
abnormalities. The large amount of data to be read and reported, with more than
100 studies per day for a single radiologist, poses a challenge in consistently
maintaining high interpretation accuracy. The introduction of large-scale
public datasets has led to a series of novel systems for automated abnormality
classification. However, the labels of these datasets were obtained using
natural language processed medical reports, yielding a large degree of label
noise that can impact the performance. In this study, we propose novel training
strategies that handle label noise from such suboptimal data. Prior label
probabilities were measured on a subset of training data re-read by 4
board-certified radiologists and were used during training to increase the
robustness of the training model to the label noise. Furthermore, we exploit
the high comorbidity of abnormalities observed in chest radiography and
incorporate this information to further reduce the impact of label noise.
Additionally, anatomical knowledge is incorporated by training the system to
predict lung and heart segmentation, as well as spatial knowledge labels. To
deal with multiple datasets and images derived from various scanners that apply
different post-processing techniques, we introduce a novel image normalization
strategy. Experiments were performed on an extensive collection of 297,541
chest radiographs from 86,876 patients, leading to a state-of-the-art
performance level for 17 abnormalities from 2 datasets. With an average AUC
score of 0.880 across all abnormalities, our proposed training strategies can
be used to significantly improve performance scores.
