DOCTOR：基于可穿戴医疗传感器的多疾病检测持续学习框架
DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors
AI总结：
本文提出DOCTOR持续学习框架，利用多头DNN、样本回放与隐私保护的合成数据生成，使单个小于350KB模型可基于可穿戴医疗传感器连续检测多种疾病并显著缓解遗忘。
摘要：
机器学习（ML）与边缘设备中可穿戴医疗传感器（WMS）的现代进展，使面向智慧医疗的ML驱动疾病检测成为可能。传统的ML驱动疾病检测方法依赖于为每种疾病及其对应的WMS数据定制单独模型。然而，此类方法缺乏对分布偏移和新任务分类类别的适应能力。此外，每出现一种新疾病，它们都需要重新设计架构并从头重新训练。而且，在边缘设备中安装多个ML模型会消耗过多内存、更快耗尽电池，并使检测过程复杂化。为应对这些挑战，我们提出DOCTOR，这是一个基于WMS的多疾病检测持续学习（CL）框架。它采用多头深度神经网络（DNN）和回放式CL算法。该CL算法使框架能够持续学习新任务，在这些任务中，不同的数据分布、分类类别和疾病检测任务被依次引入。它通过数据保留方法和合成数据生成（SDG）模块抵消灾难性遗忘。数据保留方法会保存来自先前任务中信息最丰富的真实训练数据子集，用于样本回放。SDG模块对真实训练数据的概率分布进行建模，并在保护数据隐私的同时生成用于生成式回放的合成数据。多头DNN使DOCTOR能够基于用户的WMS数据同时检测多种疾病。我们在多种CL实验中证明了DOCTOR凭借单个DNN模型保持高疾病分类准确率的有效性。在复杂场景中，DOCTOR以小于350KB的小模型体积，实现了比朴素微调框架高1.43倍的平均测试准确率、高1.25倍的F1分数，以及高0.41的后向迁移值。
英文摘要：
Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge devices have enabled ML-driven disease detection for smart healthcare. Conventional ML-driven methods for disease detection rely on customizing individual models for each disease and its corresponding WMS data. However, such methods lack adaptability to distribution shifts and new task classification classes. In addition, they need to be rearchitected and retrained from scratch for each new disease. Moreover, installing multiple ML models in an edge device consumes excessive memory, drains the battery faster, and complicates the detection process. To address these challenges, we propose DOCTOR, a multi-disease detection continual learning (CL) framework based on WMSs. It employs a multi-headed deep neural network (DNN) and a replay-style CL algorithm. The CL algorithm enables the framework to continually learn new missions where different data distributions, classification classes, and disease detection tasks are introduced sequentially. It counteracts catastrophic forgetting with a data preservation method and a synthetic data generation (SDG) module. The data preservation method preserves the most informative subset of real training data from previous missions for exemplar replay. The SDG module models the probability distribution of the real training data and generates synthetic data for generative replay while retaining data privacy. The multi-headed DNN enables DOCTOR to detect multiple diseases simultaneously based on user WMS data. We demonstrate DOCTOR's efficacy in maintaining high disease classification accuracy with a single DNN model in various CL experiments. In complex scenarios, DOCTOR achieves 1.43 times better average test accuracy, 1.25 times better F1-score, and 0.41 higher backward transfer than the naive fine-tuning framework with a small model size of less than 350KB.
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