论文标题
低功率硬件的深度学习诊断支持案例研究
Low-Power Hardware-Based Deep-Learning Diagnostics Support Case Study
论文作者
论文摘要
深度学习研究引起了广泛的兴趣，导致出现了各种各样的技术创新和应用。由于深度学习研究的很大比例关注基于视觉的应用，因此存在使用其中一些技术来实现低功率便携式医疗保健诊断支持解决方案的潜力。在本文中，我们提出了一个基于硬件的嵌入式软件诊断支持系统的实施：（a）在厚血液涂片中的疟疾，（b）痰液样品中的结核病，以及（c）粪便样品中的肠道寄生虫感染。我们使用基于挤压网络的模型来减少网络大小和计算时间。我们还利用训练有素的量化技术进一步减少了学习模型的记忆足迹。这使基于显微镜的病原体检测将实验室专家级别的精度分类为独立的嵌入式硬件平台。与传统的基于CPU的实施相比，提议的实施功率更高6倍，推理时间为$ \ sim $ 3 ms/示例。
Deep learning research has generated widespread interest leading to emergence of a large variety of technological innovations and applications. As significant proportion of deep learning research focuses on vision based applications, there exists a potential for using some of these techniques to enable low-power portable health-care diagnostic support solutions. In this paper, we propose an embedded-hardware-based implementation of microscopy diagnostic support system for PoC case study on: (a) Malaria in thick blood smears, (b) Tuberculosis in sputum samples, and (c) Intestinal parasite infection in stool samples. We use a Squeeze-Net based model to reduce the network size and computation time. We also utilize the Trained Quantization technique to further reduce memory footprint of the learned models. This enables microscopy-based detection of pathogens that classifies with laboratory expert level accuracy as a standalone embedded hardware platform. The proposed implementation is 6x more power-efficient compared to conventional CPU-based implementation and has an inference time of $\sim$ 3 ms/sample.
