InfiltrNet: 用于脑肿瘤浸润风险预测的双分支CNN-Transformer架构
InfiltrNet: Dual-Branch CNN-Transformer Architecture for Brain Tumor Infiltration Risk Prediction
AI总结：
提出InfiltrNet，结合CNN和Swin Transformer编码器及交叉注意力融合模块，从多模态MRI预测三区浸润风险图，利用距离变换生成标签，在BraTS数据集上优于五个基线模型。
摘要：
胶质瘤是侵袭性脑肿瘤，会浸润到磁共振成像（MRI）可见肿瘤边界以外的周围组织。预测这种浸润的空间范围对于手术规划和放射治疗至关重要，然而现有的深度学习方法侧重于分割可见肿瘤，而非估计周围组织的浸润风险。本文提出了InfiltrNet，一种新颖的双分支架构，通过交叉注意力融合模块将卷积神经网络（CNN）编码器与Swin Transformer编码器相结合，从多模态MRI预测三区浸润风险图。提出了一种基于距离变换的标签生成策略，从标准脑肿瘤分割（BraTS）标注中推导出可复现的浸润风险区域。InfiltrNet使用组合的Dice-CrossEntropy和边界感知损失进行训练，并在中间解码器级别添加辅助监督头。在BraTS 2020和BraTS 2025上的大量实验表明，InfiltrNet优于五个已建立的基线模型。使用GradCAM++和遮挡敏感性的可解释性分析证实，模型关注临床相关的瘤周区域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
