Summary of Efficient Multi-view Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals, by Qihan Hu et al.
Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals
by Qihan Hu, Daomiao Wang, Hong Wu, Jian Liu, Cuiwei Yang
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes two novel architectures, View-Centric Transformer (VCT) and Multitask Masked Autoencoder (M2AE), to address the limitations of traditional automatic multi-view fusion (MVF) models for cardiovascular system signals. These models efficiently handle asynchronous events and heterogeneous views by emphasizing each view’s centrality. The approach is particularly useful when real-world data arrives with incomplete views, a common scenario rarely considered in research. To adapt to missing-view scenarios, the paper introduces prompt techniques that fine-tune less than 3% of the model’s data. Experiments demonstrate the superiority of these methods over prevailing methodologies in tasks like atrial fibrillation detection and blood pressure estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to combine different views of heart signals. It uses special computer models to make sure each view is important, even if some views are missing. This helps us understand heart problems better and track people’s health more accurately. The results show that this method works well for tasks like detecting abnormal heart rhythms and measuring blood pressure. |
Keywords
» Artificial intelligence » Autoencoder » Prompt » Transformer
