Summary of Exploring Variational Autoencoders For Medical Image Generation: a Comprehensive Study, by Khadija Rais et al.
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study
by Khadija Rais, Mohamed Amroune, Abdelmadjid Benmachiche, Mohamed Yassine Haouam
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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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 provides a comprehensive review of studies on variational autoencoders (VAEs) in medical imaging, focusing on their capability to generate synthetic images similar to real data for data augmentation purposes. The study reviews various architectures and methods used to develop VAEs for medical images and compares them with other generative models like GANs regarding image quality and sample diversity. Additionally, it highlights the applications of VAEs in several medical fields, demonstrating their potential to improve segmentation and classification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how a special type of machine learning called Variational Autoencoders (VAEs) can help create fake medical images that look like real ones. This is useful for adding more data to smaller datasets or those with imbalanced classes, which improves the overall quality and accuracy of medical image analysis. The study reviews different ways to use VAEs in medical imaging and compares them to other similar techniques. It also shows how VAEs can be used in various medical fields to improve diagnosis and treatment. |
Keywords
» Artificial intelligence » Classification » Data augmentation » Machine learning
