  Obtaining automated preliminary read reports for common exams such as chest
X-rays will expedite clinical workflows and improve operational efficiencies in
hospitals. However, the quality of reports generated by current automated
approaches is not yet clinically acceptable as they cannot ensure the correct
detection of a broad spectrum of radiographic findings nor describe them
accurately in terms of laterality, anatomical location, severity, etc. In this
work, we present a domain-aware automatic chest X-ray radiology report
generation algorithm that learns fine-grained description of findings from
images and uses their pattern of occurrences to retrieve and customize similar
reports from a large report database. We also develop an automatic labeling
algorithm for assigning such descriptors to images and build a novel deep
learning network that recognizes both coarse and fine-grained descriptions of
findings. The resulting report generation algorithm significantly outperforms
the state of the art using established score metrics.
