# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 1 Introduction  
Medical images are important to diagnose and detect underlying diseases, and radiological reports are essential to aid clinical decision making [1]. They describe some observations of the image such as the extent, size, and location of the disease. The physician communicates findings and diagnoses from the patient's medical scan through the medical report. This process is often laborious, taking an average of 5 to 10 min to write a medical report [2]. The daily task of a radiologist involves analyzing a large number of medical images, which helps the physician to locate the lesion more accurately. Due to the increasing demand for medical images, radiologists still have a large workload. However, the process of writing radiology reports can be time-consuming and tedious for radiologists [3], and it can also be error-prone when writing a report. In addition, the ability to automatically generate accurate reports helps radiologists and physicians to make quick and meaningful diagnoses. Its potential efficiency and benefits can be substantial, especially in critical situations such as outbreaks of COVID or similar pandemics. In order to reduce the burden on radiologists, it is important to be able to generate reports accurately and automatically. These reasons provide a good motivation for our research into the automatic generation of medical reports. With the development of image captioning and the availability of large-scale datasets, the application of deep learning in the automatic generation of medical reports has been continuously deepened. However, how to generate accurate radiology reports is still a challenging task, because the radiology report generation task is quite different from the image captioning task. First, a radiology report is generated to output a paragraph, whichis usually composed of several sentences, while image captioning generally only needs to generate one sentence. Secondly, the generation of radiology reports requires extensive domain knowledge to generate clinically coherent text and the use of medical terms to describe normal and abnormal medical observations [4]. In addition, in the image caption, the model needs to cover all the details of the image as much as possible to generate rich captions. However, in radiology report generation, the model only needs to focus on abnormal areas and infer potential diseases to generate radiology reports. The relationship between potential diseases can determine the accuracy of the generated radiology reports. Most existing methods focus on the image-to-fluent text aspect of the medical report generation problem. These methods generally perform reasonably well in addressing verbal fluency, but their results are significantly unsatisfactory in terms of clinical accuracy. The possible reason is that their results are far from proficient in revealing the topics related to the expected diseases and symptoms in the generated texts, and they ignore the associations between these underlying diseases. Adding a knowledge graph can alleviate this problem. A knowledge graph describes the relationships between concepts, entities, and their keys in the objective world in a structured way. Knowledge graphs are also often used as prior knowledge, which can provide complementary information for accurate reporting. Medical reports typically consist of many long sentences describing various disease-related symptoms and related topics in precise and domain-specific terms, which may potentially influence each other. Deep learning methods often suffer from a lack of knowledge when not explicitly taught compared to experienced radiologists, which limits the accuracy of the generation. Modeling the associations between medical observations in the form of a knowledge graph allows us to further leverage prior knowledge to generate high-quality reports. To this end, [PERSON] et al. [5] and [PERSON] et al. [6] combined knowledge reasoning based on the knowledge graph with encoders and decoders for radiology report generation.