  Chest X-rays are the most common diagnostic exams in emergency rooms and
hospitals. There has been a surge of work on automatic interpretation of chest
X-rays using deep learning approaches after the availability of large open
source chest X-ray dataset from NIH. However, the labels are not sufficiently
rich and descriptive for training classification tools. Further, it does not
adequately address the findings seen in Chest X-rays taken in
anterior-posterior (AP) view which also depict the placement of devices such as
central vascular lines and tubes. In this paper, we present a new chest X-ray
benchmark database of 73 rich sentence-level descriptors of findings seen in AP
chest X-rays. We describe our method of obtaining these findings through a
semi-automated ground truth generation process from crowdsourcing of clinician
annotations. We also present results of building classifiers for these findings
that show that such higher granularity labels can also be learned through the
framework of deep learning classifiers.
