  Radiology report generation (RRG) aims to automatically generate free-text
descriptions from clinical radiographs, e.g., chest X-Ray images. RRG plays an
essential role in promoting clinical automation and presents significant help
to provide practical assistance for inexperienced doctors and alleviate
radiologists' workloads. Therefore, consider these meaningful potentials,
research on RRG is experiencing explosive growth in the past half-decade,
especially with the rapid development of deep learning approaches. Existing
studies perform RRG from the perspective of enhancing different modalities,
provide insights on optimizing the report generation process with elaborated
features from both visual and textual information, and further facilitate RRG
with the cross-modal interactions among them. In this paper, we present a
comprehensive review of deep learning-based RRG from various perspectives.
Specifically, we firstly cover pivotal RRG approaches based on the
task-specific features of radiographs, reports, and the cross-modal relations
between them, and then illustrate the benchmark datasets conventionally used
for this task with evaluation metrics, subsequently analyze the performance of
different approaches and finally offer our summary on the challenges and the
trends in future directions. Overall, the goal of this paper is to serve as a
tool for understanding existing literature and inspiring potential valuable
research in the field of RRG.
