结合图神经网络与Mamba以捕获全切片图像中局部与全局组织空间关系
Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images
AI总结：
本文提出将图神经网络GNN与状态空间模型Mamba结合，同时捕获全切片图像中图块间的局部与全局空间关系，并在早期肺腺癌无进展生存期预测中验证其优于多种现有聚合方法。
摘要：
在计算病理学中，从十亿像素级全切片图像（WSIs）中提取空间特征是一项基础任务，但由于其尺寸巨大，WSIs通常被分割成较小的图块。该分析的一个关键方面是聚合这些图块的信息，以在WSI级别进行预测。我们提出了一种模型，将消息传递图神经网络（GNN）与状态空间模型（Mamba）相结合，以捕获WSIs中图块之间的局部和全局空间关系。该模型的有效性在预测早期肺腺癌（LUAD）患者的无进展生存期方面得到了证明。我们将该模型与其他用于WSIs中图块级信息聚合的最先进方法进行了比较，包括基于图块级信息汇总统计的聚合、基于多实例学习（MIL）的聚合、基于GNN的聚合以及基于GNN-transformer的聚合。额外实验显示了不同类型的节点特征和不同的图块采样策略对模型性能的影响。这项工作可以轻松扩展到任何基于WSI的分析。代码：https://github.com/rina-ding/gat-mamba。
英文摘要：
In computational pathology, extracting spatial features from gigapixel whole slide images (WSIs) is a fundamental task, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of this analysis is aggregating information from these tiles to make predictions at the WSI level. We introduce a model that combines a message-passing graph neural network (GNN) with a state space model (Mamba) to capture both local and global spatial relationships among the tiles in WSIs. The model's effectiveness was demonstrated in predicting progression-free survival among patients with early-stage lung adenocarcinomas (LUAD). We compared the model with other state-of-the-art methods for tile-level information aggregation in WSIs, including tile-level information summary statistics-based aggregation, multiple instance learning (MIL)-based aggregation, GNN-based aggregation, and GNN-transformer-based aggregation. Additional experiments showed the impact of different types of node features and different tile sampling strategies on the model performance. This work can be easily extended to any WSI-based analysis. Code: https://github.com/rina-ding/gat-mamba.
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。
