Summary of Predicting Stroke Through Retinal Graphs and Multimodal Self-supervised Learning, by Yuqing Huang et al.
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised Learning
by Yuqing Huang, Bastian Wittmann, 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 The proposed approach integrates clinical information with retinal image representation to capture comprehensive cardiovascular health, leveraging large multimodal datasets for new medical insights. This contrastive framework combines graph and tabular data using vessel graphs derived from retinal images, achieving efficient representation. By integrating data from multiple sources and applying contrastive learning for transfer learning, the method significantly enhances stroke prediction accuracy. Self-supervised learning techniques enable effective learning from unlabeled data, reducing reliance on large annotated datasets. The approach showed an AUROC improvement of 3.78% from supervised to self-supervised approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us identify strokes earlier and more accurately by combining information from retinal images with clinical data. It’s like putting together a puzzle! By using special computer algorithms, scientists created a way to learn about cardiovascular health without needing huge amounts of labeled data. This new method is really good at predicting strokes and could be used in the future to help doctors make better decisions. |
Keywords
» Artificial intelligence » Self supervised » Supervised » Transfer learning
