用端到端Transformer自动生成胸部X光报告，提升临床准确性。
GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation
- 基于变压器架构，直接从X光片生成报告
- 在临床准确率指标上刷新纪录，其他指标持平领先水平
- 首次引入课程学习策略，助力模型更稳定提升
医学影像对疾病诊断、监测与治疗至关重要。放射科报告是医生记录发现的主要方式，但撰写耗时且需专业临床知识。自动化生成放射科报告有望提升医疗质量并显著减轻临床负担。本文设计并评估了一种基于端到端变压器的方法，用于生成准确且事实完整的胸部X光报告。此外，我们首次在医学影像中引入课程学习策略，并验证其对性能提升的有效性。实验基于MIMIC-CXR-JPG数据库进行，该数据集为目前最大的胸部X光数据集。结果表明，在自然语言生成（NLG）常用指标BLEU和ROUGE-L上达到当前最优水平，同时在临床准确性指标F1（example-averaged、macro、micro）及广泛使用的METEOR指标上创下新纪录。
原文摘要 · Abstract (English)
Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals attest their findings, but their writing is time consuming and requires specialized clinical expertise. The automated generation of radiography reports has thus the potential to improve and standardize patient care and significantly reduce clinicians workload. Through our work, we have designed and evaluated an end-to-end transformer-based method to generate accurate and factually complete radiology reports for X-ray images. Additionally, we are the first to introduce curriculum learning for end-to-end transformers in medical imaging and demonstrate its impact in obtaining improved performance. The experiments have been conducted using the MIMIC-CXR-JPG database, the largest available chest X-ray dataset. The results obtained are comparable with the current state-of-the-art on the natural language generation (NLG) metrics BLEU and ROUGE-L, while setting new state-of-the-art results on F1 examples-averaged, F1-macro and F1-micro metrics for clinical accuracy and on the METEOR metric widely used for NLG.
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