Summary of Ce-ssl: Computation-efficient Semi-supervised Learning For Ecg-based Cardiovascular Diseases Detection, by Rushuang Zhou et al.
CE-SSL: Computation-Efficient Semi-Supervised Learning for ECG-based Cardiovascular Diseases Detection
by Rushuang Zhou, Lei Clifton, Zijun Liu, Kannie W. Y. Chan, David A. Clifton, Yuan-Ting Zhang, Yining Dong
First submitted to arxiv on: 20 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 proposed computation-efficient semi-supervised learning paradigm (CE-SSL) tackles the label scarcity problem in automatic cardiovascular diseases (CVDs) detection using electrocardiography (ECG). This medium-level technical summary explains that CE-SSL adapts pre-trained models on downstream datasets with limited supervision while maintaining high computational efficiency. The approach combines a random-deactivation technique, one-shot rank allocation module, and lightweight semi-supervised learning pipeline to enhance model performance. Experimental results demonstrate CE-SSL outperforms state-of-the-art methods in multi-label CVDs detection with reduced GPU footprints, training time, and parameter storage space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to improve the accuracy of computers diagnosing heart conditions using ECG signals. This method uses an existing deep learning model and adapts it to work better with limited data. The new approach is more efficient than before and can process information faster, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Deep learning » One shot » Semi supervised
