Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning

ZHUO Li; ZHANG Lei; JIA Tongyao; LI Xiaoguang; ZHANG Hui

doi:10.11999/JEIT230249

Volume 46 Issue 3

Mar. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(3): 986-994

ZHUO Li, ZHANG Lei, JIA Tongyao, LI Xiaoguang, ZHANG Hui. Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 986-994. doi: 10.11999/JEIT230249

Citation:

ZHUO Li, ZHANG Lei, JIA Tongyao, LI Xiaoguang, ZHANG Hui. Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 986-994. doi: 10.11999/JEIT230249

Citation:

ZHUO Li, ZHANG Lei, JIA Tongyao, LI Xiaoguang, ZHANG Hui. Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning[J]. Journal of Electronics & Information Technology, 2024, 46(3): 986-994. doi: 10.11999/JEIT230249

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Few Shot Domain Adaptation Tongue Color Classification in Traditional Chinese Medicine via Two-stage Meta-learning

doi: 10.11999/JEIT230249

1.
Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing University of Technology, Beijing 100124, China
2.
Signal and Information Processing Laboratory, Beijing University of Technology, Beijing 100124, China

Funds: The National Natural Science Foundation of China (61871006), Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (ZYYCXTD-C-202210)

Received Date: 2023-04-11
Rev Recd Date: 2023-09-07

Available Online: 2023-09-12

Publish Date: 2024-03-27

Abstract

Abstract

Tongue color is one of the most concerning diagnostic features of tongue diagnosis in Traditional Chinese Medicine (TCM). In practical applications, the performance of the model trained from the tongue data acquired by one device is dramatically degraded when applied to other devices due to the data distribution discrepancy. Therefore, in this paper, a few shot domain adaptation tongue color classification method with two-stage meta-learning is proposed. Firstly, a two-stage meta-learning training strategy is proposed to extract domain invariant features from labeled samples in the source domain, and then, the meta-trained network model is fine-tuned using a few labeled data in the target domain, so that the model can quickly adapt to the new sample characteristics in the target domain, improving the generalization ability of the tongue color classification model and avoid overfitting problem. Next, a progressive pseudo label generation strategy is proposed, which uses the meta-trained model to predict the unlabeled samples in the target domain. The prediction results with high confidence are selected and treated as pseudo labels. So high-quality pseudo labels can be gradually generated. Finally, these high-quality pseudo labels are used to train the model, together with the labeled data in the target domain. The tongue color classification model can be obtained. Considering the noisy pseudo labels, the contrast regularization function is adopted, which can effectively suppress the negative impact of noisy samples in the training process and improve the tongue color classification accuracy in the target domain. The experimental results on two self-established TCM tongue color classification datasets show that the classification accuracy of tongue color in the target domain reaches 91.3% when only 20 labeled samples are given in the target domain, which is only 2.05% lower than that of the supervised classification model in the target domain.
- Tongue color classification in Traditional Chinese Medicine(TCM),
- Few shot,
- Domain adaptation,
- Two-stage meta-learning

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References(28)

References

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