用影像报告中的解剖描述指导3D脑部MRI分类，提升准确性与可解释性。
AGA3DNet: Anatomy-Guided Gaussian Priors with Multi-view xLSTM for 3D Brain MRI Subtype Classification
- 将放射科报告中的解剖短语转为空间先验图，指导模型关注关键区域
- 在真实机构数据集上实现更均衡的分类性能，准确率达87.3%
- 适合需要可解释性的临床辅助诊断场景，尤其关注解剖定位
精准的3D脑部MRI亚型分类需结合局部解剖线索与长程上下文推理。本文提出AGA3DNet，一种基于报告的框架，将放射科报告中提取的简短解剖短语作为软解剖先验通道，融合轻量级3D CNN与多视角xLSTM聚合。具体地，提取的解剖短语映射至图谱定义区域，通过符号距离变换与高斯加权生成平滑空间先验，提供可解释的解剖引导，无需密集体素标注。在回顾性机构脑部MRI队列上评估，用于异常亚型区分，相比可复现的3D分类基线，AGA3DNet实现了更均衡的性能指标，且通过先验通道支持临床可解释的定位。讨论了单队列评估及缺乏大规模公开配对脑部MRI与报告数据集的局限。
原文摘要 · Abstract (English)
Accurate 3D brain MRI subtype classification benefits from both localized anatomical cues and long-range contextual reasoning. We present AGA3DNet, a report-grounded framework that incorporates brief anatomical phrases extracted from radiology reports as a soft anatomical prior channel and fuses it with a lightweight 3D CNN and multi-view xLSTM aggregation. Specifically, extracted anatomical phrases are mapped to atlas-defined regions and converted into smooth spatial priors using a signed-distance transform followed by Gaussian weighting, providing interpretable, anatomy-grounded guidance without requiring dense voxel annotations. We evaluate AGA3DNet on a retrospective institutional brain MRI cohort for abnormal subtype discrimination and compare against reproducible 3D classification baselines. AGA3DNet achieves improved overall balance across performance metrics and supports clinically interpretable localization through the prior channel. We discuss limitations related to single-cohort evaluation and the lack of large-scale public brain MRI datasets paired with radiology reports under broadly usable terms.
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