Summary of Federated Gnns For Eeg-based Stroke Assessment, by Andrea Protani et al.
Federated GNNs for EEG-Based Stroke Assessment
by Andrea Protani, Lorenzo Giusti, Albert Sund Aillet, Chiara Iacovelli, Giuseppe Reale, Simona Sacco, Paolo Manganotti, Lucio Marinelli, Diogo Reis Santos, Pierpaolo Brutti, Pietro Caliandro, Luigi Serio
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method combines federated learning and Graph Neural Networks to predict stroke severity using electroencephalography signals across multiple medical institutions, addressing a regression problem by predicting the National Institutes of Health Stroke Scale. The model leverages a masked self-attention mechanism to capture salient brain connectivity patterns and employs EdgeSHAP for post-hoc explanations of neurological states after a stroke. The method achieves a mean absolute error (MAE) of 3.23 in predicting NIHSS, close to the average error made by human experts (MAE ≈ 3.0). This demonstrates the effectiveness in providing accurate and explainable predictions while maintaining data privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses machine learning to help doctors make better decisions about treating strokes. They want to be able to predict how severe a stroke is without sharing patients’ personal information. The method combines two powerful techniques: federated learning, which lets different hospitals work together on a project without sharing their data, and Graph Neural Networks, which can analyze complex brain patterns. The results show that the method is very accurate, almost as good as human experts, and it provides explanations for its decisions. This could be a big help in treating strokes. |
Keywords
» Artificial intelligence » Federated learning » Machine learning » Mae » Regression » Self attention
