Summary of Real Time Multi Organ Classification on Computed Tomography Images, by Halid Ziya Yerebakan et al.
Real Time Multi Organ Classification on Computed Tomography Images
by Halid Ziya Yerebakan, Yoshihisa Shinagawa, Gerardo Hermosillo Valadez
First submitted to arxiv on: 29 Apr 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 presents a novel approach to organ labeling in medical imaging, which is useful for various clinical automation pipelines. The proposed method, an organ classifier, identifies the selected organ without segmenting the entire volume, making it more efficient than traditional segmentation algorithms. The classifier uses a large context size and sparse data sampling strategy to generate labels in real-time. This approach can be used as an independent classifier or to produce full segmentations by querying grid locations at any resolution. The method is compared with existing segmentation techniques, demonstrating its superior runtime performance for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows a new way to quickly find the organs in medical images. This helps doctors automate some tasks and makes it easier to get important information from the images. The new method uses a special kind of computer model that can look at small parts of an image and say what organ is there. It’s fast, so it could be used for things like helping doctors diagnose diseases or making treatment plans. |
