Summary of Tsynd: Targeted Synthetic Data Generation For Enhanced Medical Image Classification, by Joshua Niemeijer et al.
TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification
by Joshua Niemeijer, Jan Ehrhardt, Hristina Uzunova, Heinz Handels
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses the challenge of training machine learning models on medical image data, which is scarce and requires costly annotations from medical professionals. To tackle this issue, generative models can be used to generate realistic synthetic data for training. However, simply choosing random synthetic samples may not be optimal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at using generative models to create synthetic medical images, making it easier to train machine learning models on large datasets. This could help make medical AI more accessible and efficient. |
Keywords
» Artificial intelligence » Machine learning » Synthetic data
