  The automated generation of radiology diagnostic reports helps radiologists
make timely and accurate diagnostic decisions while also enhancing clinical
diagnostic efficiency. However, the significant imbalance in the distribution
of data between normal and abnormal samples (including visual and textual
biases) poses significant challenges for a data-driven task like automatically
generating diagnostic radiology reports. Therefore, we propose a Dynamic
Multi-Domain Knowledge(DMDK) network for radiology diagnostic report
generation. The DMDK network consists of four modules: Chest Feature
Extractor(CFE), Dynamic Knowledge Extractor(DKE), Specific Knowledge
Extractor(SKE), and Multi-knowledge Integrator(MKI) module. Specifically, the
CFE module is primarily responsible for extracting the unprocessed visual
medical features of the images. The DKE module is responsible for extracting
dynamic disease topic labels from the retrieved radiology diagnostic reports.
We then fuse the dynamic disease topic labels with the original visual features
of the images to highlight the abnormal regions in the original visual features
to alleviate the visual data bias problem. The SKE module expands upon the
conventional static knowledge graph to mitigate textual data biases and amplify
the interpretability capabilities of the model via domain-specific dynamic
knowledge graphs. The MKI distills all the knowledge and generates the final
diagnostic radiology report. We performed extensive experiments on two widely
used datasets, IU X-Ray and MIMIC-CXR. The experimental results demonstrate the
effectiveness of our method, with all evaluation metrics outperforming previous
state-of-the-art models.
