Summary of Comprer: a Multimodal Multi-objective Pretraining Framework For Enhanced Medical Image Representation, by Guy Lutsker et al.
COMPRER: A Multimodal Multi-Objective Pretraining Framework for Enhanced Medical Image Representation
by Guy Lutsker, Hagai Rossman, Nastya Godiva, Eran Segal
First submitted to arxiv on: 4 Feb 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 In this paper, researchers develop a novel multi-modal AI framework called COMPRER that improves medical-image representation, diagnostic inferences, and disease prognosis. The framework combines multiple objectives, including multimodal loss, temporal loss, medical-measure prediction, and reconstruction loss. Contrary to expectations, the combination of these objectives boosts performance on certain tasks. The authors apply this framework to fundus images and carotid ultrasound and validate its capabilities by predicting cardiovascular conditions. COMPRER achieves higher AUC scores compared to existing models on held-out data and maintains favorable performance on an OOD dataset despite being trained on less data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary COMPRER is a new AI tool that helps doctors make better medical diagnoses and predictions. It combines information from different medical tests like images and sounds, which helps it understand patterns over time and make more accurate predictions. The tool uses special training methods to learn how to combine this information effectively. In testing, COMPRER outperformed other similar tools on many tasks, especially when working with limited data. |
Keywords
* Artificial intelligence * Auc * Multi modal
