Summary of Self-supervised Learning For Graph-structured Data in Healthcare Applications: a Comprehensive Review, by Safa Ben Atitallah et al.
Self-Supervised Learning for Graph-Structured Data in Healthcare Applications: A Comprehensive Review
by Safa Ben Atitallah, Chaima Ben Rabah, Maha Driss, Wadii Boulila, Anis Koubaa
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 reviews self-supervised learning (SSL) approaches designed specifically for graph-structured data in healthcare applications. It explores challenges and opportunities associated with healthcare data and assesses the effectiveness of SSL techniques in real-world healthcare applications, including disease prediction, medical image analysis, and drug discovery. The authors critically evaluate the performance of different SSL methods across these tasks, highlighting their strengths, limitations, and potential future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews how to use self-learning computer algorithms for healthcare data that includes connections between things, like people or medical tests. It looks at how well this works in real-world situations like predicting diseases, analyzing images, and finding new medicines. The authors compare different approaches to see what works best and what could be improved. |
Keywords
* Artificial intelligence * Self supervised
