Radiology report generation (RRG) aims to automatically generate free-text descriptions from clinical radiographs, e.g., chest X-Ray images. RRG plays an essential role in promoting clinical automation and presents significant help to provide practical assistance for inexperienced doctors and alleviate radiologists' workloads. Therefore, consider these meaningful potentials, research on RRG is experiencing explosive growth in the past half-decade, especially with the rapid development of deep learning approaches. Existing studies perform RRG from the perspective of enhancing different modalities, provide insights on optimizing the report generation process with elaborated features from both visual and textual information, and further facilitate RRG with the cross-modal interactions among them. In this paper, we present a comprehensive review of deep learning-based RRG from various perspectives. Specifically, we firstly cover pivotal RRG approaches based on the task-specific features of radiographs, reports, and the cross-modal relations between them, and then illustrate the benchmark datasets conventionally used for this task with evaluation metrics, subsequently analyze the performance of different approaches and finally offer our summary on the challenges and the trends in future directions. Overall, the goal of this paper is to serve as a tool for understanding existing literature and inspiring potential valuable research in the field of RRG.

翻译：放射学报告生成旨在从临床放射影像（例如胸部X光图像）中自动生成自由文本描述。该任务在促进临床自动化方面发挥关键作用，并为经验不足的医生提供实用帮助，同时减轻放射科医生的工作负担。鉴于其重要潜力，近五年来关于放射学报告生成的研究呈爆发式增长，尤其是随着深度学习方法的快速发展。现有研究从增强不同模态的角度展开，通过优化视觉与文本信息的精炼特征来改进报告生成过程，并进一步借助跨模态交互促进生成质量。本文从多角度对基于深度学习的放射学报告生成进行了全面综述。具体而言，我们首先综述了基于放射影像、报告及其跨模态关系的核心方法，随后介绍该任务常用基准数据集及评估指标，接着分析不同方法的性能，最后总结当前面临的挑战与未来趋势。总体而言，本文旨在为理解现有文献提供工具，并激发该领域潜在有价值的研究。