Summary of Synthetic Privileged Information Enhances Medical Image Representation Learning, by Lucas Farndale et al.
Synthetic Privileged Information Enhances Medical Image Representation Learning
by Lucas Farndale, Chris Walsh, Robert Insall, Ke Yuan
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Tissues and Organs (q-bio.TO)
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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 A multimodal self-supervised representation learning approach is shown to excel in medical image analysis, yielding strong task performance and biologically informed insights. However, this method heavily relies on large, paired datasets, which can be limiting when such data is scarce or non-existent. In contrast, image generation methods can thrive on very small datasets and find mappings between unpaired datasets, effectively generating synthetic paired data. This work demonstrates that representation learning can be significantly improved by synthetically generating paired information, outperforming single-modality training (up to 4.4x error reduction) or authentic multi-modal paired dataset training (up to 5.6x error reduction). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Medical image analysis uses a self-supervised approach to learn representations from images. This method works well when there’s lots of data, but it can be tricky when there’s not much data available. A new idea is to generate fake data that looks like real data, and then use this fake data to train the model. This helps the model learn more accurately and make better predictions. |
Keywords
* Artificial intelligence * Image generation * Multi modal * Representation learning * Self supervised
