无解码器的监督超像素图神经网络用于多模态MRI中脑肿瘤的准确定位
Decoder-Free Supervoxel GNN for Accurate Brain-Tumor Localization in Multi-Modal MRI
AI总结：
本文提出SVGFormer，一种无解码器的监督超像素图神经网络，用于多模态MRI中脑肿瘤的准确定位，通过层次编码器结合Transformer和图注意力网络，实现高精度和可解释的医学图像分析。
摘要：
现代3D医学影像的视觉骨干通常通过参数密集的编码器-解码器结构处理密集的体素网格，这种设计将大量参数分配给空间重建而不是特征学习。我们的方法引入了SVGFormer，一种无解码器的流程，基于一个内容感知的分组阶段，将体积分割成语义图的超像素。其层次编码器通过结合片段级的Transformer和超像素级的图注意力网络，共同建模细粒度的区域内部特征和更广泛的区域间依赖性。这种设计将所有可学习的容量集中在特征编码上，并提供了从片段到区域级别的内在、双尺度可解释性。为了验证框架的灵活性，我们在BraTS数据集上训练了两个专用模型：一个用于节点级分类，另一个用于肿瘤比例回归。两个模型都取得了优异的性能，分类模型的F1分数为0.875，回归模型的MAE为0.028，证实了编码器学习判别和局部化特征的能力。我们的结果表明，基于图的、仅编码器的范式为3D医学图像表示提供了一种准确且内在可解释的替代方案。
英文摘要：
Modern vision backbones for 3D medical imaging typically process dense voxel grids through parameter-heavy encoder-decoder structures, a design that allocates a significant portion of its parameters to spatial reconstruction rather than feature learning. Our approach introduces SVGFormer, a decoder-free pipeline built upon a content-aware grouping stage that partitions the volume into a semantic graph of supervoxels. Its hierarchical encoder learns rich node representations by combining a patch-level Transformer with a supervoxel-level Graph Attention Network, jointly modeling fine-grained intra-region features and broader inter-regional dependencies. This design concentrates all learnable capacity on feature encoding and provides inherent, dual-scale explainability from the patch to the region level. To validate the framework's flexibility, we trained two specialized models on the BraTS dataset: one for node-level classification and one for tumor proportion regression. Both models achieved strong performance, with the classification model achieving a F1-score of 0.875 and the regression model a MAE of 0.028, confirming the encoder's ability to learn discriminative and localized features. Our results establish that a graph-based, encoder-only paradigm offers an accurate and inherently interpretable alternative for 3D medical image representation.
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