Summary of Self-supervised Pretraining For Cardiovascular Magnetic Resonance Cine Segmentation, by Rob A. J. De Mooij et al.
Self-supervised Pretraining for Cardiovascular Magnetic Resonance Cine Segmentation
by Rob A. J. de Mooij, Josien P. W. Pluim, Cian M. Scannell
First submitted to arxiv on: 26 Sep 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 This paper investigates the effectiveness of self-supervised pretraining (SSP) for automated cardiovascular magnetic resonance (CMR) short-axis cine segmentation. The authors evaluate various SSP methods and their impact on segmentation performance. They find that SSP can improve segmentation accuracy, but inconsistent results in previous studies have made it challenging to apply this approach to CMR. To address this, the study aims to provide a comprehensive evaluation of SSP for CMR cine segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence (AI) can help doctors automatically identify important parts on heart scans. The researchers test different ways AI is trained without labeled data and see if it’s good at finding heart features. They find that AI can be helpful, but previous studies didn’t agree on this. So, they want to figure out which way of training AI works best for this task. |
Keywords
» Artificial intelligence » Pretraining » Self supervised
