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- GitHub - aikunyi MedGNN: Official implementation of the paper Towards . . .
MedGNN (WWW 2025) The repo is the official implementation for the paper: "Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification"
- MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for . . .
To address these limitations, we propose a Multi-resolution Spatiotemporal Graph Learning framework, MedGNN, for medical time series classification Specifically, we first propose to construct multi-resolution adaptive graph structures to learn dynamic multi-scale embeddings
- MedGNN: General Medical Image Recognition Network via GNN Visual . . .
Experiments show that MedGNN achieves strong competitive performance across various 2D and 3D medical image recognition datasets Moreover, it visualizes lesion relationships through graphs, enabling interpretable analysis based on graph structures
- 2025时间序列都有哪些创新点可做——总结篇 - 知乎
MedGNN:Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification 方法： 论文提出了一种名为MedGNN的框架，用于医学时间序列分类。
- MedGNN: General Medical Image Recognition Network via GNN Visual . . .
To address this, we transform medical images into graph structures and propose MedGNN, a general recognition network based on Graph Neural Network (GNN) visual representations
- MedGNN: General Medical Image Recognition Network via GNN Visual . . .
To address this, we transform medical images into graph structures and propose MedGNN, a general recognition network based on Graph Neural Network (GNN) visual representations
- MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for . . .
We present results of our proposed MedGNN compared to several representative baselines with two different experimental setup (e g , sample-based and subject-based evaluation) in Tables 1 and 2
- MedGNN README. md at main · aikunyi MedGNN · GitHub
MedGNN (WWW 2025) The repo is the official implementation for the paper: "Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification"
- MedGNN: Capturing the Links Between Urban Characteristics and Medical . . .
To address this, we propose MedGNN, a spatio-topologically explicit framework that constructs a 2-hop spatial graph, integrating positional and locational node embeddings with urban characteristics in a graph neural network
- MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for . . .
Notably, MedGNN ranks first in terms of F1 score across all five datasets, demonstrating that MedGNN is not only accurate but also achieves a good balance between precision and recall, in-dicating its exceptional robustness in handling classification tasks
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