Summary of Isimed: a Framework For Self-supervised Learning Using Intrinsic Spatial Information in Medical Images, by Nabil Jabareen et al.
ISImed: A Framework for Self-Supervised Learning using Intrinsic Spatial Information in Medical Images
by Nabil Jabareen, Dongsheng Yuan, Sören Lukassen
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The proposed ISImed method uses Self-Supervised Learning (SSL) to learn interpretable representations in medical images. By leveraging the similarity of human body structures across multiple images, the method creates a self-supervised objective that captures spatial information. This is achieved by sampling image crops and comparing learned representation vectors to true distances between them. The intuition is that the learned latent space encodes positionality for each crop. The method is evaluated on two medical imaging datasets using state-of-the-art SSL benchmarking methods, showing efficient learning of representations that capture underlying structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how computers can learn from medical images without human help. They do this by looking at how similar different parts of the body are across many images. This helps them create a map of where things are in each image. The method is tested on two big collections of medical images and does well compared to other state-of-the-art methods. |
Keywords
» Artificial intelligence » Latent space » Self supervised
