Zhang Shiqian, Cui Yong, Xu Dandan, Lin Yusong
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, China.
Collaborative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, China.
PeerJ Comput Sci. 2025 Mar 5;11:e2708. doi: 10.7717/peerj-cs.2708. eCollection 2025.
The popularity and convenience of mobile medical image analysis and diagnosis in mobile edge computing (MEC) environments have greatly improved the efficiency and quality of healthcare services, necessitating the use of deep neural networks (DNNs) for image analysis. However, DNNs face performance and energy constraints when operating on the mobile side, and are limited by communication costs and privacy issues when operating on the edge side, and previous edge-end collaborative approaches have shown unstable performance and low search efficiency when exploring classification strategies. To address these issues, we propose a DNN edge-optimized collaborative inference strategy (MOCI) for medical image diagnosis, which optimizes data transfer and computation allocation by combining compression techniques and multi-agent reinforcement learning (MARL) methods. The MOCI strategy first uses coding and quantization-based compression methods to reduce the redundancy of image data during transmission at the edge, and then dynamically segments the DNN model through MARL and executes it collaboratively between the edge and the mobile device. To improve policy stability and adaptability, MOCI introduces the optimal transmission distance (Wasserstein) to optimize the policy update process, and uses the long short-term memory (LSTM) network to improve the model's adaptability to dynamic task complexity. The experimental results show that the MOCI strategy can effectively solve the collaborative inference task of medical image diagnosis and significantly reduce the latency and energy consumption with less than a 2% loss in classification accuracy, with a maximum reduction of 38.5% in processing latency and 71% in energy consumption compared to other inference strategies. In real-world MEC scenarios, MOCI has a wide range of potential applications that can effectively promote the development and application of intelligent healthcare.
移动医疗图像分析与诊断在移动边缘计算（MEC）环境中的普及和便利性极大地提高了医疗服务的效率和质量，因此需要使用深度神经网络（DNN）进行图像分析。然而，DNN在移动端运行时面临性能和能量限制，在边缘端运行时受到通信成本和隐私问题的限制，并且以往的边缘端协作方法在探索分类策略时表现出不稳定的性能和较低的搜索效率。为了解决这些问题，我们提出了一种用于医学图像诊断的DNN边缘优化协作推理策略（MOCI），该策略通过结合压缩技术和多智能体强化学习（MARL）方法来优化数据传输和计算分配。MOCI策略首先使用基于编码和量化的压缩方法在边缘端减少图像数据传输过程中的冗余，然后通过MARL动态分割DNN模型并在边缘端和移动设备之间协同执行。为了提高策略的稳定性和适应性，MOCI引入最优传输距离（Wasserstein）来优化策略更新过程，并使用长短期记忆（LSTM）网络提高模型对动态任务复杂性的适应性。实验结果表明，MOCI策略能够有效解决医学图像诊断的协作推理任务，显著降低延迟和能耗，分类准确率损失不到2%，与其他推理策略相比，处理延迟最多可降低38.5%，能耗最多可降低71%。在实际的MEC场景中，MOCI具有广泛的潜在应用，可以有效推动智能医疗的发展和应用。
