预训练、推理、基准测试：基于CheXpert Plus数据集的X射线报告生成

中文摘要 AI 辅助

基于X射线图像的放射学报告生成（RRG）是医学人工智能领域中的一个关键研究方向，具有减轻临床医生诊断负担和缩短患者等待时间的巨大潜力。尽管近年来取得了显著进展，该领域仍面临明显的瓶颈，主要源于标准化基准的不足以及通用大模型领域适应性不足的问题。值得注意的是，新发布的CheXpert Plus数据集未附带相应的基线实现和评估结果，这阻碍了后续算法的标准化训练、定量评估和公平比较。为缓解这一局限，我们建立了一个全面的基准，涵盖在CheXpert Plus上现有的X射线报告生成模型和大语言模型。该基准为后续方法提供了可靠的比较基础，使研究人员能够快速识别该领域内的最先进方法。除基准构建外，我们在大模型范式下重新思考了X射线RRG，并提出了一种名为MambaXray-PRB的新框架。我们的框架通过多阶段大模型预训练和多模态思维链推理，提高了报告生成性能并增强了模型的可解释性。该流程由三个连续阶段组成：自监督自回归建模、X射线-报告对比学习，以及针对推理和报告生成的后训练优化。在IU X射线、MIMIC-CXR和CheXpert Plus数据集上的大量实验验证了MambaXray-PRB在放射学报告生成中的有效性。本文的源代码可在该https URL上获取。

英文摘要

X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potential to alleviate clinicians' diagnostic workload and shorten patient waiting periods. Despite substantial advances over recent years, the field faces evident bottlenecks stemming from insufficient standardized benchmarks and inadequate domain adaptation of generic large models. Notably, the newly released CheXpert Plus dataset is provided without accompanying baseline implementations and evaluation results, which impedes standardized training, quantitative evaluation and fair comparison among follow-up algorithms. To mitigate this limitation, we establish a comprehensive benchmark encompassing prevailing X-ray report generation models and Large Language Models on CheXpert Plus. This benchmark delivers a reliable comparative foundation for upcoming methods and enables researchers to rapidly identify state-of-the-art approaches within this domain. Beyond benchmark construction, we rethink X-ray RRG under the paradigm of large models and propose a novel framework termed MambaXray-PRB. Our framework improves report generation performance and enhances model interpretability via multi-stage large-model pre-training and multi-modal Chain-of-Thought reasoning. The pipeline consists of three successive phases: self-supervised auto-regressive modeling, X-ray-report contrastive learning, and post-training optimization for reasoning and report generation. Extensive experiments on IU X-ray, MIMIC-CXR, and CheXpert Plus datasets validate the effectiveness of MambaXray-PRB for radiology report generation. The source code of this paper is available on https://github.com/Event-AHU/Medical_Image_Analysis

发表机构

• Anhui University（安徽大学）
• Peng Cheng Laboratory（鹏城实验室）
• Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
• Peking University（北京大学）

机构由 AI 辅助整理，请以论文原文为准。