Core Concepts
The author introduces a novel method, LM-RRG, that combines large models with clinical quality reinforcement learning to generate accurate and comprehensive chest X-ray radiology reports. The approach aims to address the shortcomings of current methods by emphasizing specific regions with medical significance and incorporating a novel clinical quality reinforcement learning strategy.
Abstract
The content discusses the development of LM-RRG for radiology report generation, highlighting the integration of large models and reinforcement learning to enhance accuracy. The method involves a feature extractor, multimodal report generator, and clinical quality reinforcement learning. Experiments on MIMIC-CXR and IU-Xray datasets demonstrate superior performance over existing methods.
Key points:
Introduction of LM-RRG for accurate chest X-ray radiology reports.
Utilization of large language models for feature extraction.
Multimodal prompts construction for report generation.
Implementation of clinical quality reinforcement learning using RadCliQ metric.
Superior performance demonstrated through experiments on two datasets.
Stats
Extensive experiments on MIMIC-CXR and IU-Xray datasets demonstrate the superiority of our method over the state of the art.
RadCliQ metric is used as a reward function in the learning process.