医疗保健中的对抗性多源迁移学习：应用于糖尿病患者的血糖预测
Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People
摘要：
深度学习尚未彻底改变医疗保健的一般实践，尽管在一些特定任务上取得了可喜的成果。这部分是由于数据量不足，影响了模型的训练。为了解决这一问题，可以通过使用迁移学习利用其异质性来合并来自多个卫生行为者或患者的数据。为了提高多个数据源之间的传输质量，我们提出了一个多源对抗性迁移学习框架，该框架能够学习跨源相似的特征表示，因此更通用，更容易转移。我们将这一想法应用于糖尿病患者的血糖预测，使用完全卷积神经网络。评估是通过探索不同的传输方案与三个数据集的特点是他们的高帧间和帧内变异。虽然知识转移总体上是有益的，但我们表明，通过使用对抗性训练方法，可以进一步提高统计和临床准确性，超越当前最先进的结果。特别是，当使用来自不同数据集的数据时，或者当数据集内的数据太少时，它会大放异彩。为了理解模型的行为，我们分析了学习的特征表示，并在这方面提出了一个新的度量。与标准转移相反，对抗性转移不会区分患者和数据集，有助于学习更一般的特征表示。对抗性训练框架改进了多源环境中一般特征表示的学习，增强了对未知目标的知识转移。所提出的方法可以帮助提高不同健康参与者在深度模型训练中共享数据的效率。
英文摘要：
Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on their heterogeneity through the use of transfer learning. To improve the quality of the transfer between multiple sources of data, we propose a multi-source adversarial transfer learning framework that enables the learning of a feature representation that is similar across the sources, and thus more general and more easily transferable. We apply this idea to glucose forecasting for diabetic people using a fully convolutional neural network. The evaluation is done by exploring various transfer scenarios with three datasets characterized by their high inter and intra variability. While transferring knowledge is beneficial in general, we show that the statistical and clinical accuracies can be further improved by using of the adversarial training methodology, surpassing the current state-of-the-art results. In particular, it shines when using data from different datasets, or when there is too little data in an intra-dataset situation. To understand the behavior of the models, we analyze the learnt feature representations and propose a new metric in this regard. Contrary to a standard transfer, the adversarial transfer does not discriminate the patients and datasets, helping the learning of a more general feature representation. The adversarial training framework improves the learning of a general feature representation in a multi-source environment, enhancing the knowledge transfer to an unseen target. The proposed method can help improve the efficiency of data shared by different health actors in the training of deep models.
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