Summary of Runtime Freezing: Dynamic Class Loss For Multi-organ 3d Segmentation, by James Willoughby et al.
Runtime Freezing: Dynamic Class Loss for Multi-Organ 3D Segmentation
by James Willoughby, Irina Voiculescu
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel approach to improve segmentation performance in medical imaging, particularly when multiple organs are segmented simultaneously. The authors address the challenge of class imbalance, which is common in medical datasets where one organ may be much more abundant than others. They introduce dynamic class-based loss strategies that adapt to the imbalanced training data, leading to improved segmentation results on a challenging Multi-Class 3D Abdominal Organ dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make medical images better by improving how they segment organs. It’s hard to do when there are many organs and some are much more common than others. The authors come up with new ways to make the training data fairer, so the computer can learn to see the different organs better. This makes a big difference on a special dataset that has multiple organs. |
