The rapid increase of computed tomography (CT) scans and their time-consuming manual analysis have created an urgent need for robust automated analysis techniques in clinical settings. These aim to assist radiologists and help them managing their growing workload. Existing methods typically generate entire reports directly from 3D CT images, without explicitly focusing on observed abnormalities. This unguided approach often results in repetitive content or incomplete reports, failing to prioritize anomaly-specific descriptions. We propose a new anomaly-guided report generation model, which first predicts abnormalities and then generates targeted descriptions for each. Evaluation on a public dataset demonstrates significant improvements in report quality and clinical relevance. We extend our work by conducting an ablation study to demonstrate its effectiveness.

翻译：计算机断层扫描（CT）的快速增长及其耗时的手动分析，使得临床环境中对鲁棒的自动化分析技术产生了迫切需求。这些技术旨在协助放射科医师，并帮助他们应对日益增长的工作负荷。现有方法通常直接从三维CT图像生成完整报告，而未明确聚焦于观察到的异常。这种无引导的方法常导致内容重复或报告不完整，未能优先考虑针对异常的具体描述。我们提出了一种新的异常引导报告生成模型，该模型首先预测异常，然后为每个异常生成针对性描述。在公开数据集上的评估表明，报告质量和临床相关性均有显著提升。我们通过消融研究进一步验证了该模型的有效性。