Department of Computer and Software Engineering, College of Electrical and Mechanical, National University of Sciences and Technology, Islamabad, Pakistan.
College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
PLoS One. 2022 Jan 6;17(1):e0262209. doi: 10.1371/journal.pone.0262209. eCollection 2022.
The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. A new research direction is gaining increasing attention that involves the use of hybrid approaches based on natural language processing and computer vision techniques to create auto medical report generation systems. The auto report generator, producing radiology reports, will significantly reduce the burden on doctors and assist them in writing manual reports. Because the sensitivity of chest X-ray (CXR) findings provided by existing techniques not adequately accurate, producing comprehensive explanations for medical photographs remains a difficult task. A novel approach to address this issue was proposed, based on the continuous integration of convolutional neural networks and long short-term memory for detecting diseases, followed by the attention mechanism for sequence generation based on these diseases. Experimental results obtained by using the Indiana University CXR and MIMIC-CXR datasets showed that the proposed model attained the current state-of-the-art efficiency as opposed to other solutions of the baseline. BLEU-1, BLEU-2, BLEU-3, and BLEU-4 were used as the evaluation metrics.
自动化生成放射学报告提供 X 光片，具有极大的潜力增强患者疾病的临床诊断。一种新的研究方向越来越受到关注，即利用基于自然语言处理和计算机视觉技术的混合方法来创建自动医疗报告生成系统。自动报告生成器可以生成放射学报告，这将大大减轻医生的负担，并帮助他们编写手动报告。由于现有技术提供的胸部 X 光 (CXR) 结果的敏感性不够准确，因此为医学照片生成全面的解释仍然是一项艰巨的任务。提出了一种新的方法来解决这个问题，该方法基于卷积神经网络和长短时记忆的连续集成，用于检测疾病，然后基于这些疾病使用注意力机制进行序列生成。使用印第安纳大学 CXR 和 MIMIC-CXR 数据集进行的实验结果表明，与基线的其他解决方案相比，所提出的模型达到了当前的最先进的效率。BLEU-1、BLEU-2、BLEU-3 和 BLEU-4 被用作评估指标。
