用量化技术让医疗影像模型在边缘设备上高效运行
Resource-efficient medical image classification for edge devices
- 采用量化感知训练和后训练量化优化模型
- 模型体积和推理延迟大幅降低，实现实时处理
- 适合部署在资源受限的偏远医疗场景
医疗图像分类对精准及时诊断至关重要，但在计算和内存受限的边缘设备上部署深度学习模型面临挑战。本研究提出一种资源高效的医疗图像分类方法，通过模型量化技术降低参数与激活精度，显著减少计算开销和内存占用，同时保持分类准确率。重点优化了适用于边缘设备的量化感知训练（QAT）和后训练量化（PTQ）方法，并在多个医学影像数据集上分析其性能影响。实验结果表明，量化模型在模型尺寸和推理延迟方面均有显著降低，可在边缘硬件上实现实时处理，同时维持临床可接受的诊断准确率。该工作为在偏远及资源有限地区部署AI医疗诊断提供了可行路径，提升了医疗技术的可及性与可扩展性。
原文摘要 · Abstract (English)
Medical image classification is a critical task in healthcare, enabling accurate and timely diagnosis. However, deploying deep learning models on resource-constrained edge devices presents significant challenges due to computational and memory limitations. This research investigates a resource-efficient approach to medical image classification by employing model quantization techniques. Quantization reduces the precision of model parameters and activations, significantly lowering computational overhead and memory requirements without sacrificing classification accuracy. The study focuses on the optimization of quantization-aware training (QAT) and post-training quantization (PTQ) methods tailored for edge devices, analyzing their impact on model performance across medical imaging datasets. Experimental results demonstrate that quantized models achieve substantial reductions in model size and inference latency, enabling real-time processing on edge hardware while maintaining clinically acceptable diagnostic accuracy. This work provides a practical pathway for deploying AI-driven medical diagnostics in remote and resource-limited settings, enhancing the accessibility and scalability of healthcare technologies.
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