Bringing deep learning techniques to electromagnetic imaging is of interest considering its great success in various fields. Deep neural nets however are known for being data hungry machines, and in many practical cases, such as electromagnetic medical imaging, there is not enough to feed them. Scarcity of data necessitates reliance on simulations to generate a sufficiently large dataset for deep learning to perform any complicated task. Simulations however, can not perfectly represent real environments and therefore, any neural net trained on simulation data will invariably fail when evaluated on real data. This work customizes a deep domain adaptation technique for matching distributions of complex-valued electromagnetic data. We demonstrate the advantage of using complex-valued models over regular ones. An operational neural network trained on simulation data and adapted to practical data to perform brain injury localization is presented.

中文翻译：

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通过深度神经网络缩小脑中风电磁成像模拟与现实的差距


考虑到深度学习技术在各个领域的巨大成功，将其引入电磁成像是很有趣的。然而，深度神经网络因需要大量数据而闻名，在许多实际情况下，例如电磁医学成像，没有足够的数据来满足它们。数据稀缺需要依赖模拟来生成足够大的数据集，以便深度学习执行任何复杂的任务。然而，模拟无法完美地代表真实环境，因此，任何在模拟数据上训练的神经网络在对真实数据进行评估时都将不可避免地失败。这项工作定制了一种深度域适应技术，用于匹配复值电磁数据的分布。我们展示了使用复值模型相对于常规模型的优势。提出了一种根据模拟数据进行训练并适应实际数据以执行脑损伤定位的操作神经网络。