Summary of Ncdd: Nearest Centroid Distance Deficit For Out-of-distribution Detection in Gastrointestinal Vision, by Sandesh Pokhrel et al.
NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision
by Sandesh Pokhrel, Sanjay Bhandari, Sharib Ali, Tryphon Lambrou, Anh Nguyen, Yash Raj Shrestha, Angus Watson, Danail Stoyanov, Prashnna Gyawali, Binod Bhattarai
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to integrating deep learning tools with gastrointestinal vision has the potential to revolutionize diagnosis, treatment, and patient care. Despite this promise, existing methods are plagued by overconfidence in predictions, even when faced with novel or unknown disease patterns, thereby compromising their reliability. To mitigate this issue, researchers propose a new framework that leverages advanced computer vision techniques to improve model robustness and reduce overfitting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how combining deep learning tools with gastrointestinal vision can transform diagnosis and treatment. Right now, these tools often make predictions they’re not sure about, which can be bad news for patients. To solve this problem, the researchers are working on a new way to use computer vision that will help models be more accurate and reliable. |
Keywords
» Artificial intelligence » Deep learning » Overfitting
