Summary of Gaussian Process Emulators For Few-shot Segmentation in Cardiac Mri, by Bruno Viti and Franz Thaler and Kathrin Lisa Kapper and Martin Urschler and Martin Holler and Elias Karabelas
Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI
by Bruno Viti, Franz Thaler, Kathrin Lisa Kapper, Martin Urschler, Martin Holler, Elias Karabelas
First submitted to arxiv on: 11 Nov 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 A new method for segmentation of cardiac magnetic resonance images (MRI) combines few-shot learning with a U-Net architecture and Gaussian Process Emulators (GPEs). This approach reduces reliance on labeled data, making it suitable for diagnosing and treating various cardiovascular diseases. The merged method uses GPEs to learn the relationship between support images and masks in latent space, enabling segmentation of unseen query images from small labeled support sets at inference. Compared to state-of-the-art unsupervised and few-shot methods, this approach shows higher DICE coefficients, particularly when the size of the support set is small. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to analyze cardiac MRI images using a combination of machine learning techniques. It helps doctors diagnose and treat heart problems by improving image segmentation. The method uses a type of AI called few-shot learning, which can work with very little labeled data. This makes it useful for tasks that require quick analysis or diagnosis. The researchers tested their approach on a public dataset and found it outperformed other methods in many cases. |
Keywords
» Artificial intelligence » Few shot » Image segmentation » Inference » Latent space » Machine learning » Unsupervised
