Summary of Vizecgnet: Visual Ecg Image Network For Cardiovascular Diseases Classification with Multi-modal Training and Knowledge Distillation, by Ju-hyeon Nam et al.
VizECGNet: Visual ECG Image Network for Cardiovascular Diseases Classification with Multi-Modal Training and Knowledge Distillation
by Ju-Hyeon Nam, Seo-Hyung Park, Su Jung Kim, Sang-Chul Lee
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 A novel deep learning model called VizECGNet is proposed for determining the prognosis of various cardiovascular diseases using only printed electrocardiogram (ECG) graphics. This approach leverages cross-modal attention modules to integrate information from both image and signal modalities, as well as self-modality attention modules to capture long-range dependencies in each modality. The model also employs knowledge distillation to improve the similarity between predictions from each modality stream. During inference, VizECGNet uses only ECG images, achieving higher performance than traditional signal-based models on precision, recall, and F1-Score metrics by 3.50%, 8.21%, and 7.38%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors use electrocardiograms (ECGs) to check the heart’s health. Usually, ECG data is stored as pictures or digital signals. While many deep learning models can analyze digital signals, hospitals often prefer storing images because it’s cheaper. But what if we could use those image files to predict heart problems? A new AI model called VizECGNet can do just that! It looks at the pictures and uses special attention modules to find important details. This innovative approach is better than traditional methods, helping doctors make more accurate predictions. |
Keywords
» Artificial intelligence » Attention » Deep learning » F1 score » Inference » Knowledge distillation » Precision » Recall
