Summary of Privacy-preserving Sam Quantization For Efficient Edge Intelligence in Healthcare, by Zhikai Li et al.
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare
by Zhikai Li, Jing Zhang, Qingyi Gu
First submitted to arxiv on: 14 Sep 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 The proposed Data-Free Quantization Framework for Segment Anything Model (DFQ-SAM) addresses the challenges of deploying AI models on resource-limited edge devices while preserving medical data privacy. By leveraging pseudo-positive label evolution and patch similarity, DFQ-SAM synthesizes high-quality data without requiring original data, eliminating data transfer risks. The framework also incorporates scale reparameterization to ensure accurate low-bit quantization. Experimental results demonstrate significant performance improvements on various datasets, enabling secure, fast, and personalized healthcare services at the edge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI technology can help bridge the gap in healthcare resources worldwide. A new model called SAM excels in medical monitoring and diagnosis. However, it’s too big to fit on small devices like those used in hospitals. To solve this problem, researchers developed a way to shrink the model without using the original data. This keeps sensitive medical information safe from hackers. The new method is called DFQ-SAM. It uses fake data that’s almost as good as real data and ensures the compressed model works well even on low-power devices. |
Keywords
» Artificial intelligence » Quantization » Sam
