  Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG)
because it reveals the relations among diseases and thus can be utilized to
guide the generation process. However, constructing a comprehensive KG is
labor-intensive and its applications on the MRG process are under-explored. In
this study, we establish a complete KG on chest X-ray imaging that includes 137
types of diseases and abnormalities. Based on this KG, we find that the current
MRG data sets exhibit a long-tailed problem in disease distribution. To
mitigate this problem, we introduce a novel augmentation strategy that enhances
the representation of disease types in the tail-end of the distribution. We
further design a two-stage MRG approach, where a classifier is first trained to
detect whether the input images exhibit any abnormalities. The classified
images are then independently fed into two transformer-based generators,
namely, ``disease-specific generator" and ``disease-free generator" to generate
the corresponding reports. To enhance the clinical evaluation of whether the
generated reports correctly describe the diseases appearing in the input image,
we propose diverse sensitivity (DS), a new metric that checks whether generated
diseases match ground truth and measures the diversity of all generated
diseases. Results show that the proposed two-stage generation framework and
augmentation strategies improve DS by a considerable margin, indicating a
notable reduction in the long-tailed problem associated with under-represented
diseases.
