Summary of Transfer Learning in Ecg Diagnosis: Is It Effective?, by Cuong V. Nguyen and Cuong D.do
Transfer Learning in ECG Diagnosis: Is It Effective?
by Cuong V. Nguyen, Cuong D.Do
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 abstract presents a comprehensive study investigating the effectiveness of transfer learning in multi-label ECG classification. By comparing fine-tuning performance with training from scratch on various ECG datasets and deep neural networks, researchers found that fine-tuning is the preferred choice for small downstream datasets but training from scratch can achieve comparable performance when the dataset is large enough. The study also highlights the compatibility of transfer learning with convolutional neural networks versus recurrent neural networks, which are commonly used in time-series ECG applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ECG diagnosis uses deep learning to help doctors diagnose heart conditions. But there isn’t much data available for real-world situations. To fix this, researchers use something called “transfer learning.” It helps by using what’s already learned from bigger datasets. The study looked at how well transfer learning works in diagnosing ECGs with multiple labels (different types of problems). They found that fine-tuning is better when there isn’t much data, but if there’s enough data, training from scratch can work just as well. They also discovered that certain kinds of neural networks work better with transfer learning than others. |
Keywords
* Artificial intelligence * Classification * Deep learning * Fine tuning * Time series * Transfer learning
