Summary of How to Select Slices For Annotation to Train Best-performing Deep Learning Segmentation Models For Cross-sectional Medical Images?, by Yixin Zhang and Kevin Kramer and Maciej A. Mazurowski
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?
by Yixin Zhang, Kevin Kramer, Maciej A. Mazurowski
First submitted to arxiv on: 11 Dec 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 The paper proposes a method for selecting slices from cross-sectional medical images to maximize the performance of deep learning-based segmentation models. The authors conducted experiments on four medical imaging tasks with varying annotation budgets, numbers of annotated cases, and slice selection techniques. They found that by carefully selecting slices based on certain criteria, they could achieve better model performance using fewer annotations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors and researchers make the most of their time and money when labeling medical images. It shows how to pick the right parts of an image to help a computer learn to identify different things, like organs or tumors. The authors tested different approaches on four different tasks and found that some methods work better than others. |
Keywords
* Artificial intelligence * Deep learning
