用Mamba模型提升医学影像报告生成效率与质量
R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation
- 结合Mamba的高效序列处理与Transformer的上下文理解能力
- 在超21万对影像报告数据上实现更高质量与更低计算开销
- 适合医疗AI落地场景，尤其关注推理速度与资源消耗的团队
放射科报告生成在医学影像中至关重要，但人工标注耗时费力，亟需自动化方法。现有研究多采用Transformer生成报告，但计算成本高，限制实际应用。本文提出R2Gen-Mamba，一种新型自动报告生成方法，利用Mamba的高效序列处理能力，同时保留Transformer的上下文优势。由于Mamba计算复杂度更低，R2Gen-Mamba不仅提升了训练与推理效率，还生成了高质量报告。在包含超过21万对X光图像-报告的两个基准数据集上的实验表明，该方法在报告质量与计算效率方面均优于多个先进方法。源代码可在线获取。
原文摘要 · Abstract (English)
Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly utilizes Transformers to generateradiology reports, which can be computationally intensive,limiting their use in real applications. In this work, we presentR2Gen-Mamba, a novel automatic radiology report genera-tion method that leverages the efficient sequence processingof the Mamba with the contextual benefits of Transformerarchitectures. Due to lower computational complexity ofMamba, R2Gen-Mamba not only enhances training and in-ference efficiency but also produces high-quality reports.Experimental results on two benchmark datasets with morethan 210,000 X-ray image-report pairs demonstrate the ef-fectiveness of R2Gen-Mamba regarding report quality andcomputational efficiency compared with several state-of-the-art methods. The source code can be accessed online.
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