针对3D MRI中神经侵犯边界预测难题，提出自适应局部感知网络
LoSA-Net: A Localized and Scale-Adaptive Network for Boundary-Sensitive Prediction of Perineural Invasion in 3D MRI
- 采用局部自注意力与多尺度深度卷积，精准保留神经结构细节
- 在168例胆管癌患者数据上达到0.7567 AUC，优于主流模型
- 适合医学影像中微弱边界特征的高精度检测任务
神经侵犯（PNI）是肿瘤侵袭性的临床重要指标，影响手术决策，推动了术前可靠评估的需求。然而，PNI的细微MRI特征常与周围解剖结构相似，难以非侵入性识别。常规体积模型中的下采样或过度全局特征聚合易削弱这些细小线索。本文提出LoSA-Net，一种用于3D MRI中边界敏感型PNI预测的局部化与尺度自适应网络。通过对话邻域注意力（TNA）在头维度混合中保持神经对齐的细节，利用多尺度深度处理调节感受野的尺度自适应特征融合（SAFM），并通过跨尺度精炼与对齐（CSRA）在不同阶段维持语义上下文与高分辨率边界的统一。在168例胆管癌患者的增强MRI扫描中，LoSA-Net达到0.7567 AUC，优于在相同预处理与优化设置下的代表性卷积与Transformer基线模型。
原文摘要 · Abstract (English)
Perineural invasion (PNI) is a clinically relevant indicator of tumor aggressiveness and can influence surgical decision-making, motivating interest in reliable preoperative assessment. The subtle MRI features of PNI, however, often resemble nearby anatomy, complicating noninvasive prediction. These fine perineural cues are easily attenuated by routine downsampling or overly global feature aggregation, reducing the effectiveness of conventional volumetric models. We present LoSA-Net, a localized and scale-adaptive architecture for boundary-sensitive PNI prediction in 3D MRI. Talking Neighborhood Attention (TNA) preserves nerve-aligned detail through localized self-attention with head-wise mixing, and Scale-Adaptive Feature Mixing (SAFM) modulates the receptive field using multi-scale depthwise processing. Cross-Scale Refinement and Alignment (CSRA) maintains consistency between semantic context and high-resolution boundaries across stages. In contrast-enhanced MRI scans from 168 patients with cholangiocarcinoma, LoSA-Net achieves an AUC of 0.7567 and outperforms representative convolutional and transformer baselines under matched preprocessing and optimization settings.
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