用关键病灶区域引导生成更准确的胸部X光报告
Semantically Informed Salient Regions Guided Radiology Report Generation
- 通过跨模态语义识别医学关键病灶区域
- 在图像建模和报告生成中聚焦病灶，提升准确性
- 适合临床辅助诊断系统开发人员使用
基于深度学习的胸部X光自动报告生成技术有望显著减轻放射科医生的工作负担。然而，由于医学影像中异常通常细微且分布稀疏，现有方法常生成流畅但不准确的报告，限制了临床应用。为此，我们提出语义引导的关键区域生成方法（SISRNet）。该方法通过细粒度跨模态语义明确识别具有医学重要性的显著区域，并在图像建模与报告生成阶段系统性关注这些高信息量区域，有效捕捉细微异常，缓解数据偏差的负面影响，最终生成临床准确的报告。在常用IU-Xray和MIMIC-CXR数据集上，SISRNet表现优于现有方法。
原文摘要 · Abstract (English)
Recent advances in automated radiology report generation from chest X-rays using deep learning algorithms have the potential to significantly reduce the arduous workload of radiologists. However, due to the inherent massive data bias in radiology images, where abnormalities are typically subtle and sparsely distributed, existing methods often produce fluent yet medically inaccurate reports, limiting their applicability in clinical practice. To address this issue effectively, we propose a Semantically Informed Salient Regions-guided (SISRNet) report generation method. Specifically, our approach explicitly identifies salient regions with medically critical characteristics using fine-grained cross-modal semantics. Then, SISRNet systematically focuses on these high-information regions during both image modeling and report generation, effectively capturing subtle abnormal findings, mitigating the negative impact of data bias, and ultimately generating clinically accurate reports. Compared to its peers, SISRNet demonstrates superior performance on widely used IU-Xray and MIMIC-CXR datasets.
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