用新模型融合脑瘤影像与病理图，实现无创分子分型预测
Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Prediction
- 基于纱层理论构建多模态融合框架，保留跨模态结构信息
- 在数据缺失时仍保持高精度，准确率优于现有方法
- 适合想做脑瘤无创诊断或多模态学习的研究者
胶质母细胞瘤是一种高度侵袭性脑肿瘤，进展迅速。近年来研究表明，其分子亚型分类可作为有效靶向治疗选择的重要生物标志物。然而，当前分类需通过侵入性组织活检完成全面组织病理分析。现有的结合MRI与组织病理图像的多模态方法仍有限，且缺乏有效机制来保留跨模态的共享结构信息。特别是，基于图的模型常无法保留异质图中的判别特征，而处理模态缺失或不完整数据的结构重建机制也未被充分探索。为此，我们提出一种新颖的纱层（sheaf-based）框架，实现结构感知且一致的MRI与组织病理数据融合。该模型在基准方法上表现更优，并在模态缺失或不完整场景下展现出强鲁棒性，有助于推动虚拟活检工具的发展以实现快速诊断。源代码已公开于 https://github.com/basiralab/MMSN/。
原文摘要 · Abstract (English)
Glioblastoma is a highly invasive brain tumor with rapid progression rates. Recent studies have shown that glioblastoma molecular subtype classification serves as a significant biomarker for effective targeted therapy selection. However, this classification currently requires invasive tissue extraction for comprehensive histopathological analysis. Existing multimodal approaches combining MRI and histopathology images are limited and lack robust mechanisms for preserving shared structural information across modalities. In particular, graph-based models often fail to retain discriminative features within heterogeneous graphs, and structural reconstruction mechanisms for handling missing or incomplete modality data are largely underexplored. To address these limitations, we propose a novel sheaf-based framework for structure-aware and consistent fusion of MRI and histopathology data. Our model outperforms baseline methods and demonstrates robustness in incomplete or missing data scenarios, contributing to the development of virtual biopsy tools for rapid diagnostics. Our source code is available at https://github.com/basiralab/MMSN/.
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