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.

本研究解决了医学影像报告生成的耗时和专业化问题，提出了一种新的端对端变压器方法来生成准确、内容完整的X射线报告。通过在MIMIC-CXR-JPG数据库上进行实验，采用课程学习的创新方法，该研究在多项临床准确性指标上取得了最先进的结果，展示了对提高患者护理标准化的潜在影响。

GIT-CXR：用于胸部X射线报告生成的端对端变压器