Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare

TL;DR

This paper introduces DFQ-SAM, a data-free quantization method for the Segment Anything Model that preserves privacy and enables efficient deployment of AI in resource-limited healthcare edge devices.

Contribution

It proposes a novel data-free quantization framework with pseudo-label evolution and scale reparameterization, improving model compression without compromising data privacy.

Findings

DFQ-SAM achieves high segmentation accuracy with low-bit quantization.

The method effectively preserves data privacy by eliminating the need for original data during calibration.

Extensive experiments demonstrate significant performance improvements across various datasets.

Abstract

The disparity in healthcare personnel expertise and medical resources across different regions of the world is a pressing social issue. Artificial intelligence technology offers new opportunities to alleviate this issue. Segment Anything Model (SAM), which excels in intelligent image segmentation, has demonstrated exceptional performance in medical monitoring and assisted diagnosis. Unfortunately, the huge computational and storage overhead of SAM poses significant challenges for deployment on resource-limited edge devices. Quantization is an effective solution for model compression; however, traditional methods rely heavily on original data for calibration, which raises widespread concerns about medical data privacy and security. In this paper, we propose a data-free quantization framework for SAM, called DFQ-SAM, which learns and calibrates quantization parameters without any original data, thus effectively preserving data privacy during model compression. Specifically, we propose pseudo-positive label evolution for segmentation, combined with patch similarity, to fully leverage the semantic and distribution priors in pre-trained models, which facilitates high-quality data synthesis as a substitute for real data. Furthermore, we introduce scale reparameterization to ensure the accuracy of low-bit quantization. We perform extensive segmentation experiments on various datasets, and DFQ-SAM consistently provides significant performance on low-bit quantization. DFQ-SAM eliminates the need for data transfer in cloud-edge collaboration, thereby protecting sensitive data from potential attacks. It enables secure, fast, and personalized healthcare services at the edge, which enhances system efficiency and optimizes resource allocation, and thus facilitating the pervasive application of artificial intelligence in worldwide healthcare.