# Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Prediction

arXiv: https://arxiv.org/abs/2508.09717 · HTML: https://mlsanity.com/paper/2508.09717/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs](https://mlsanity.com/paper/2202.04579/) (2022)
- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [Sheaf Neural Networks](https://mlsanity.com/paper/2012.06333/) (2020)
- [Graph Convolutional Networks for Graphs Containing Missing Features](https://mlsanity.com/paper/2007.04583/) (2020)
- [Learning on Multimodal Graphs: A Survey](https://mlsanity.com/paper/2402.05322/) (2024)
- [Heterogeneous Graph Learning for Multi-modal Medical Data Analysis](https://mlsanity.com/paper/2211.15158/) (2022)
- [Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks](https://mlsanity.com/paper/2109.12946/) (2021)
- [MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network](https://mlsanity.com/paper/2210.03779/) (2022)
- [Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs](https://mlsanity.com/paper/2309.08160/) (2023)

## Abstract (arXiv)

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/.