Summary of Navigating Data Scarcity Using Foundation Models: a Benchmark Of Few-shot and Zero-shot Learning Approaches in Medical Imaging, by Stefano Woerner and Christian F. Baumgartner
Navigating Data Scarcity using Foundation Models: A Benchmark of Few-Shot and Zero-Shot Learning Approaches in Medical Imaging
by Stefano Woerner, Christian F. Baumgartner
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper investigates the application of modern machine learning techniques, particularly few-shot learning (FSL) and zero-shot learning (ZSL), to clinical tasks. The study focuses on foundation models’ suitability for FSL and ZSL in medical image analysis tasks with limited data availability. To achieve this, 16 pretrained foundation models are benchmarked on 19 diverse medical imaging datasets using both ZSL and FSL approaches. The results show that BiomedCLIP performs best when training set sizes are very small, while large CLIP models perform better with slightly more training samples. Additionally, fine-tuning a ResNet-18 model pretrained on ImageNet achieves similar performance with more than five training examples per class. These findings emphasize the need for further research on foundation models tailored for medical applications and the collection of more datasets to train these models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In short, this study looks at how well certain machine learning models work when they have very little data to learn from. This is important because many medical tasks don’t have a lot of data available. The researchers tested 16 different models on 19 different types of medical images and found that some models do much better than others depending on the amount of data they get. They also found that one model, called ResNet-18, can be fine-tuned to work almost as well as some of the more advanced models. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Machine learning » Resnet » Zero shot
