Deep sparse transfer learning for remote smart tongue diagnosis

Xu Zhang; Wei Huang; Jing Gao; Dapeng Wang; Changchuan Bai; Zhikui Chen; Xu Zhang; Wei Huang; Jing Gao; Dapeng Wang; Changchuan Bai; Zhikui Chen

doi:10.3934/mbe.2021063

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 2: 1169-1186. doi: 10.3934/mbe.2021063

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Research article Special Issues

Deep sparse transfer learning for remote smart tongue diagnosis

1.
The School of Software Technology, Dalian University of Technology, Dalian 116620, China
2.
Department of Critical Care Medicine, First Affiliated Hospital of Dalian Medical University, Dalian 116620, China
3.
First Affiliated Hospital of Dalian Medical University, Dalian 116620, China
4.
Dalian Hospital of Traditional Chinese Medicine, Dalian 116620, China

Received: 24 November 2020 Accepted: 21 December 2020 Published: 12 January 2021

People are exploring new ideas based on artificial intelligent infrastructures for immediate processing, in which the main obstacles of widely-deploying deep methods are the huge volume of neural network and the lack of training data. To meet the high computing and low latency requirements in modeling remote smart tongue diagnosis with edge computing, an efficient and compact deep neural network design is necessary, while overcoming the vast challenge on modeling its intrinsic diagnosis patterns with the lack of clinical data. To address this challenge, a deep transfer learning model is proposed for the effective tongue diagnosis, based on the proposed similar-sparse domain adaptation (SSDA) scheme. Concretely, a transfer strategy of similar data is introduced to efficiently transfer necessary knowledge, overcoming the insufficiency of clinical tongue images. Then, to generate simplified structure, the network is pruned with transferability remained in domain adaptation. Finally, a compact model combined with two sparse networks is designed to match limited edge device. Extensive experiments are conducted on the real clinical dataset. The proposed model can use fewer training data samples and parameters to produce competitive results with less power and memory consumptions, making it possible to widely run smart tongue diagnosis on low-performance infrastructures.
- deep transfer learning,
- domain adaptation,
- pruning,
- sparse network,
- remote diagnosis,
- traditional Chinese medicine
Citation: Xu Zhang, Wei Huang, Jing Gao, Dapeng Wang, Changchuan Bai, Zhikui Chen. Deep sparse transfer learning for remote smart tongue diagnosis[J]. Mathematical Biosciences and Engineering, 2021, 18(2): 1169-1186. doi: 10.3934/mbe.2021063

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Abstract

People are exploring new ideas based on artificial intelligent infrastructures for immediate processing, in which the main obstacles of widely-deploying deep methods are the huge volume of neural network and the lack of training data. To meet the high computing and low latency requirements in modeling remote smart tongue diagnosis with edge computing, an efficient and compact deep neural network design is necessary, while overcoming the vast challenge on modeling its intrinsic diagnosis patterns with the lack of clinical data. To address this challenge, a deep transfer learning model is proposed for the effective tongue diagnosis, based on the proposed similar-sparse domain adaptation (SSDA) scheme. Concretely, a transfer strategy of similar data is introduced to efficiently transfer necessary knowledge, overcoming the insufficiency of clinical tongue images. Then, to generate simplified structure, the network is pruned with transferability remained in domain adaptation. Finally, a compact model combined with two sparse networks is designed to match limited edge device. Extensive experiments are conducted on the real clinical dataset. The proposed model can use fewer training data samples and parameters to produce competitive results with less power and memory consumptions, making it possible to widely run smart tongue diagnosis on low-performance infrastructures.

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