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The Similar Sparse Domain Adaptation Illustrated by the case of TCM Tongue Inspection

机译：由中医检查的情况说明了类似的稀疏域适应

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摘要

More attention is paid to personal health accompanying by the development of society and the change of lifestyle. Not limited in disease, the sub-health is bedeviling humanity more generally. An increasing number of people go in quest of Traditional Chinese Medicine (TCM) for life quality, since TCM achieves the significant and curative effectiveness in recuperating certain sub-health conditions. However, the lack of clinical data poses a vast challenge on the emerging deep-learning-based methods in modeling TCM diagnosis. In this paper, a Similar Sparse Domain Adaptation (SSDA) method is proposed in modeling the tongue inspection, which is one of the four diagnostic methods and plays important roles in TCM primary diagnosis. First, a similar domain adaptation is introduced to transfer necessary knowledge efficiently and overcome insufficient data. Then, inspired by the Lottery Ticket hypothesis, the network is pruned to generate sparse subnet using in adaptation. Finally, the model with two combined sparse network is designed. Extensive experiments are conducted on the real clinical data set collected in Dalian, China. Proposed model uses fewer training data samples and parameters, while consuming less power and memory, which make it easier to store and run on low-power hardware systems for widely promoting.
机译：通过社会的发展和生活方式的变化，伴随着个人健康的更多关注。疾病不受限制，亚健康更普遍地嫉妒人类。越来越多的人参加了生命质量的中药（TCM），因为TCM实现了恢复某些亚健康状况的显着和疗效。然而，缺乏临床数据对新兴的基于深度学习的方法构成了巨大的挑战，在模拟中医诊断中的基于深度学习的方法。在本文中，提出了一种类似的稀疏域适应（SSDA）方法在建模舌检查中，这是四种诊断方法之一，并在中医初步诊断中起重要作用。首先，引入了类似的域适应以有效地传输必要的知识并克服数据不足。然后，由彩票假设的启发，修剪网络以在适应中生成稀疏子网。最后，设计了具有两个组合稀疏网络的模型。广泛的实验是在中国大连收集的真实临床数据集上进行的实验。建议的模型使用较少的训练数据样本和参数，同时消耗更少的电源和内存，这使得在低功耗硬件系统上更容易地存储和运行，以广泛推广。

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