Summary of Enhancing Cardiovascular Disease Prediction Through Multi-modal Self-supervised Learning, by Francesco Girlanda et al.
Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised Learning
by Francesco Girlanda, Olga Demler, Bjoern Menze, Neda Davoudi
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a multi-modal approach to predicting cardiovascular diseases, combining cardiac magnetic resonance images, electrocardiogram signals, and medical information. By leveraging shared information across modalities, the method enables a more holistic understanding of an individual’s cardiovascular health. The model uses self-supervised learning techniques and integrates information from multiple sources to improve disease prediction with limited annotated datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors predict when someone might get heart problems earlier so they can help them sooner. It does this by combining different kinds of medical data, like special x-rays and heart monitor readings, with other health information. By looking at all these pieces together, the approach gets a better picture of a person’s overall heart health. This is helpful because it can make predictions even when there isn’t much labeled data. |
Keywords
» Artificial intelligence » Multi modal » Self supervised
