Abstract：
A radiology report plays a crucial role in guiding patient treatment, but writing these reports is a time-consuming task that demands a radiologist\'s expertise. In response to this challenge, researchers in the subfields of artificial intelligence for healthcare have explored techniques for automatically interpreting radiographic images and generating free-text reports, while much of the research on medical report creation has focused on image captioning methods without adequately addressing particular report aspects. This study introduces a Conditional Self Attention Memory-Driven Transformer model for generating radiological reports. The model operates in two phases: initially, a multi-label classification model, utilizing ResNet152 v2 as an encoder, is employed for feature extraction and multiple disease diagnosis. In the second phase, the Conditional Self Attention Memory-Driven Transformer serves as a decoder, utilizing self-attention memory-driven transformers to generate text reports. Comprehensive experimentation was conducted to compare existing and proposed techniques based on Bilingual Evaluation Understudy (BLEU) scores ranging from 1 to 4. The model outperforms the other state-of-the-art techniques by increasing the BLEU 1 (0.475), BLEU 2 (0.358), BLEU 3 (0.229), and BLEU 4 (0.165) respectively. This study\'s findings can alleviate radiologists\' workloads and enhance clinical workflows by introducing an autonomous radiological report generation system.
摘要：
放射学报告在指导患者治疗中起着至关重要的作用,但撰写这些报告是一项耗时的任务，需要放射科医师的专业知识。为了应对这一挑战，医疗保健人工智能子领域的研究人员已经探索了自动解释放射线图像和生成自由文本报告的技术，虽然许多关于医学报告创建的研究都集中在图像字幕方法上，而没有充分解决特定的报告方面。本研究介绍了一种用于生成放射学报告的条件自注意记忆驱动变压器模型。该模型分为两个阶段：最初，多标签分类模型，利用ResNet152v2作为编码器，用于特征提取和多疾病诊断。在第二阶段,条件自注意记忆驱动转换器用作解码器，利用自注意记忆驱动变压器生成文本报告。进行了综合实验，以比较基于1至4的双语评估基础（BLEU）分数的现有和拟议技术。该模型通过增加BLEU1(0.475)优于其他最先进的技术，BLEU2(0.358),BLEU3（0.229），和BLEU4（0.165）。这项研究的发现可以减轻放射科医生的工作量，并通过引入自主放射报告生成系统来提高临床工作流程。
