GIT-CXR: End-to-End Transformer for Chest X-Ray Report Generation

Abstract

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.

References in corpus (5)

• Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
• On the Automatic Generation of Medical Imaging Reports
• Interactive and Explainable Region-guided Radiology Report Generation
• Improving Chest X-Ray Report Generation by Leveraging Warm Starting
• Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty