5/1/2023 0 Comments Final draft tagger 2![]() ![]() 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. 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. First, a radiology report is generated to output a paragraph, which is usually composed of several sentences, while image captioning generally only needs to generate one sentence. 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. 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. These reasons provide a good motivation for our research into the automatic generation of medical reports. ![]() In order to reduce the burden on radiologists, it is important to be able to generate reports accurately and automatically. Its potential efficiency and benefits can be substantial, especially in critical situations such as outbreaks of COVID or similar pandemics. In addition, the ability to automatically generate accurate reports helps radiologists and physicians to make quick and meaningful diagnoses. However, the process of writing radiology reports can be time-consuming and tedious for radiologists, and it can also be error-prone when writing a report. ![]() Due to the increasing demand for medical images, radiologists still have a large workload. The daily task of a radiologist involves analyzing a large number of medical images, which helps the physician to locate the lesion more accurately. This process is often laborious, taking an average of 5 to 10 min to write a medical report. The physician communicates findings and diagnoses from the patient’s medical scan through the medical report. They describe some observations of the image such as the extent, size, and location of the disease. Medical images are important to diagnose and detect underlying diseases, and radiological reports are essential to aid clinical decision making. Our experiments show that our method outperforms state-of-the-art methods with the help of a knowledge graph constituted by prior knowledge of the patient. We evaluate the performance of the proposed method using metrics from natural language generation and clinical efficacy on two public datasets. These disease situational representations with prior knowledge are fed into the generator for self-supervised learning to generate radiology reports. Then, this feature is used as the input of the adjacency matrix of the knowledge graph, and the graph neural network is used to aggregate and transfer the information between each node to generate the situational representation of the disease with prior knowledge. The patient’s chest X-ray image and clinical history file were used as input to extract the image–text hybrid features. In this work, we mine the associations between medical discoveries in the given texts and construct a knowledge graph based on the associations between medical discoveries. Our paper focuses on the automatic generation of medical reports from input chest X-ray images. Existing deep learning methods often ignore the interplay between medical findings, which may be a bottleneck limiting the quality of generated radiology reports. However, writing radiology reports is a critical and time-consuming task for radiologists. In clinical diagnosis, radiological reports are essential to guide the patient’s treatment. ![]()
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