用双分支模型预测脑瘤浸润风险，助力精准放疗与手术规划。
InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction
- 融合CNN与Swin Transformer，通过交叉注意力融合多模态MRI特征
- 在BraTS 2020和2025上优于5个基准模型，实现三区浸润风险图预测
- 可解释性分析显示模型关注临床相关瘤周区域，适合医学影像分析研究者
胶质瘤是侵袭性强的脑肿瘤，其浸润范围常超出磁共振成像（MRI）可见边界。准确预测浸润范围对术前规划和放疗至关重要，但现有深度学习方法多聚焦于可见肿瘤分割，而非周围组织的浸润风险估计。本文提出InfiltrNet，一种新型双分支架构，将卷积神经网络（CNN）编码器与Swin Transformer编码器通过交叉注意力融合模块结合，从多模态MRI中预测三区浸润风险图。提出基于距离变换的标签生成策略，从标准脑肿瘤分割（BraTS）标注中衍生出可复现的浸润风险区域。InfiltrNet采用组合的Dice-交叉熵损失与边界感知损失，并在解码器中间层引入辅助监督头进行训练。在BraTS 2020和BraTS 2025上的大量实验表明，InfiltrNet显著优于五个主流基线模型。使用GradCAM++和遮挡敏感性分析的可解释性研究证实，模型关注临床相关的瘤周区域。
原文摘要 · Abstract (English)
Gliomas are aggressive brain tumors that infiltrate surrounding tissue beyond the visible tumor margins observed on Magnetic Resonance Imaging (MRI). Predicting the spatial extent of this infiltration is essential for surgical planning and radiation therapy, yet existing deep learning approaches focus on segmenting the visible tumor rather than estimating infiltration risk in the surrounding tissue. This paper presents InfiltrNet, a novel dual-branch architecture that combines a convolutional neural network (CNN) encoder with a Swin Transformer encoder through cross-attention fusion modules to predict three-zone infiltration risk maps from multimodal MRI. A label generation strategy based on distance transforms is proposed to derive reproducible infiltration risk zones from standard Brain Tumor Segmentation (BraTS) annotations. InfiltrNet is trained with a combined Dice-CrossEntropy and boundary-aware loss augmented by auxiliary supervision heads at intermediate decoder levels. Extensive experiments on BraTS 2020 and BraTS 2025 demonstrate that InfiltrNet outperforms five established baselines. Explainability analysis using GradCAM++ and Occlusion sensitivity confirms that the model attends to clinically relevant peritumoral regions.
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