  Automatic radiology reporting has great clinical potential to relieve
radiologists from heavy workloads and improve diagnosis interpretation.
Recently, researchers have enhanced data-driven neural networks with medical
knowledge graphs to eliminate the severe visual and textual bias in this task.
The structures of such graphs are exploited by using the clinical dependencies
formed by the disease topic tags via general knowledge and usually do not
update during the training process. Consequently, the fixed graphs can not
guarantee the most appropriate scope of knowledge and limit the effectiveness.
To address the limitation, we propose a knowledge graph with Dynamic structure
and nodes to facilitate medical report generation with Contrastive Learning,
named DCL. In detail, the fundamental structure of our graph is pre-constructed
from general knowledge. Then we explore specific knowledge extracted from the
retrieved reports to add additional nodes or redefine their relations in a
bottom-up manner. Each image feature is integrated with its very own updated
graph before being fed into the decoder module for report generation. Finally,
this paper introduces Image-Report Contrastive and Image-Report Matching losses
to better represent visual features and textual information. Evaluated on
IU-Xray and MIMIC-CXR datasets, our DCL outperforms previous state-of-the-art
models on these two benchmarks.
