Summary of Jscds: a Core Data Selection Method with Jason-shannon Divergence For Caries Rgb Images-efficient Learning, by Peiliang Zhang and Yujia Tong and Chenghu Du and Chao Che and Yongjun Zhu
JSCDS: A Core Data Selection Method with Jason-Shannon Divergence for Caries RGB Images-Efficient Learning
by Peiliang Zhang, Yujia Tong, Chenghu Du, Chao Che, Yongjun Zhu
First submitted to arxiv on: 29 Jun 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 Medium Difficulty summary: This paper proposes a novel method for efficient caries detection using deep learning-based RGB caries detection, which is crucial for preventing oral diseases. The approach, called Core Data Selection Method with Jensen-Shannon Divergence (JSCDS), aims to enhance training efficiency without compromising model performance. JSCDS calculates the mutual information between data samples and cluster centers, capturing nonlinear dependencies among high-dimensional data. Extensive experiments on RGB caries datasets show that JSCDS outperforms other data selection methods in prediction performance and time consumption, with its performance advantage becoming more pronounced when using 70% of the core data. The proposed method can be applied to various image classification tasks and has potential applications in dental diagnostics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper is about a new way to use computers to detect tooth cavities (caries) in pictures. Right now, doctors have to look at lots of pictures to find the cavities, which takes a long time. The new method, called JSCDS, helps computers find the cavities more quickly and accurately by looking for patterns in the pictures. The scientists tested this method on many pictures and found that it works better than other methods. This could help doctors diagnose tooth cavities more quickly and prevent bigger problems from happening. |
Keywords
» Artificial intelligence » Deep learning » Image classification
