Background and objectives: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

背景和目标：尽管在某些特定任务上取得了可喜的成果，但深度学习尚未彻底改变医疗保健领域的常规做法。部分原因是由于数据量不足，影响了模型的训练。为了解决这个问题，可以通过利用迁移学习来利用多个卫生参与者或患者的异质性来合并其数据。

方法：为了提高多个数据源之间传输的质量，我们提出了一种多源对抗传输学习框架，该框架能够学习跨源相似的特征表示，因此更通用，更易于传输。我们将此思想应用到使用全卷积神经网络的糖尿病人血糖预测中。评估是通过探索具有高内部和内部变异性的三个数据集的各种传输方案来完成的。

结果：虽然知识的转移总体上是有益的，但我们表明，使用对抗性训练方法可以超越当前的最新结果，进一步提高统计和临床准确性。特别是，当使用来自不同数据集的数据时，或者在数据集内情况下数据太少时，它会发光。为了了解模型的行为，我们分析了学习到的特征表示并在这方面提出了新的度量标准。与标准转移相反，对抗转移不会区分患者和数据集，有助于学习更通用的特征表示。

结论：对抗训练框架改善了在多源环境中通用特征表示的学习，增强了知识向未知目标的传递。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。