CRRG-CLIP：胸部放射学报告自动生成与胸部X光片分类
CRRG-CLIP: Automatic Generation of Chest Radiology Reports and Classification of Chest Radiographs
AI总结：
提出端到端CRRG-CLIP模型，结合Faster R-CNN、GPT-2与CLIP完成胸部X光片报告生成和分类，在多项指标上优于基线及现有方法。
摘要：
堆叠成像的复杂性和海量X光片使放射学报告的撰写变得复杂且低效。即使是经验丰富的放射科医生，在长时间高强度工作下也难以保持判读X光片的准确性和一致性。为解决这些问题，本研究提出CRRG-CLIP模型（Chest Radiology Report Generation and Radiograph Classification Model，胸部放射学报告生成与X光片分类模型），这是一个用于自动报告生成和X光片分类的端到端模型。该模型由两个模块组成：放射学报告生成模块和X光片分类模块。生成模块使用Faster R-CNN识别X光片中的解剖区域，使用二分类器选择关键区域，并使用GPT-2生成语义连贯的报告。分类模块采用无监督的Contrastive Language Image Pretraining（CLIP）模型，以应对标注数据集成本高昂和特征不足的挑战。结果表明，生成模块在BLEU、METEOR和ROUGE-L指标上表现与高性能基线模型相当，并在BLEU-2、BLEU-3、BLEU-4和ROUGE-L指标上优于GPT-4o模型。分类模块在AUC和Accuracy上显著超越当前最先进模型。这表明所提出模型在报告生成方面实现了高准确性、可读性和流畅性，同时利用未标注X光片-报告对进行多模态对比训练提升了分类性能。
英文摘要：
The complexity of stacked imaging and the massive number of radiographs make writing radiology reports complex and inefficient. Even highly experienced radiologists struggle to maintain accuracy and consistency in interpreting radiographs under prolonged high-intensity work. To address these issues, this work proposes the CRRG-CLIP Model (Chest Radiology Report Generation and Radiograph Classification Model), an end-to-end model for automated report generation and radiograph classification. The model consists of two modules: the radiology report generation module and the radiograph classification module. The generation module uses Faster R-CNN to identify anatomical regions in radiographs, a binary classifier to select key regions, and GPT-2 to generate semantically coherent reports. The classification module uses the unsupervised Contrastive Language Image Pretraining (CLIP) model, addressing the challenges of high-cost labelled datasets and insufficient features. The results show that the generation module performs comparably to high-performance baseline models on BLEU, METEOR, and ROUGE-L metrics, and outperformed the GPT-4o model on BLEU-2, BLEU-3, BLEU-4, and ROUGE-L metrics. The classification module significantly surpasses the state-of-the-art model in AUC and Accuracy. This demonstrates that the proposed model achieves high accuracy, readability, and fluency in report generation, while multimodal contrastive training with unlabelled radiograph-report pairs enhances classification performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
