  Chest X-ray is the most common medical imaging exam used to assess multiple
pathologies. Automated algorithms and tools have the potential to support the
reading workflow, improve efficiency, and reduce reading errors. With the
availability of large scale data sets, several methods have been proposed to
classify pathologies on chest X-ray images. However, most methods report
performance based on random image based splitting, ignoring the high
probability of the same patient appearing in both training and test set. In
addition, most methods fail to explicitly incorporate the spatial information
of abnormalities or utilize the high resolution images. We propose a novel
approach based on location aware Dense Networks (DNetLoc), whereby we
incorporate both high-resolution image data and spatial information for
abnormality classification. We evaluate our method on the largest data set
reported in the community, containing a total of 86,876 patients and 297,541
chest X-ray images. We achieve (i) the best average AUC score for published
training and test splits on the single benchmarking data set (ChestX-Ray14),
and (ii) improved AUC scores when the pathology location information is
explicitly used. To foster future research we demonstrate the limitations of
the current benchmarking setup and provide new reference patient-wise splits
for the used data sets. This could support consistent and meaningful
benchmarking of future methods on the largest publicly available data sets.
