Tongue Color Classification in TCM with Noisy Labels via Confident-Learning-Assisted Knowledge Distillation

LI Yanping; ZHUO Li; SUN Liangliang; ZHANG Hui; LI Xiaoguang; YANG Yang; WEI Wei

doi:10.23919/cje.2022.00.040

Volume 32 Issue 1

Jan. 2023

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LI Yanping, ZHUO Li, SUN Liangliang, ZHANG Hui, LI Xiaoguang, YANG Yang, WEI Wei. Tongue Color Classification in TCM with Noisy Labels via Confident-Learning-Assisted Knowledge Distillation[J]. Chinese Journal of Electronics, 2023, 32(1): 140-150. doi: 10.23919/cje.2022.00.040

Citation:

LI Yanping, ZHUO Li, SUN Liangliang, ZHANG Hui, LI Xiaoguang, YANG Yang, WEI Wei. Tongue Color Classification in TCM with Noisy Labels via Confident-Learning-Assisted Knowledge Distillation[J]. Chinese Journal of Electronics, 2023, 32(1): 140-150. doi: 10.23919/cje.2022.00.040

Citation:

LI Yanping, ZHUO Li, SUN Liangliang, ZHANG Hui, LI Xiaoguang, YANG Yang, WEI Wei. Tongue Color Classification in TCM with Noisy Labels via Confident-Learning-Assisted Knowledge Distillation[J]. Chinese Journal of Electronics, 2023, 32(1): 140-150. doi: 10.23919/cje.2022.00.040

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Tongue Color Classification in TCM with Noisy Labels via Confident-Learning-Assisted Knowledge Distillation

doi: 10.23919/cje.2022.00.040

LI Yanping^{1, 2
,},
ZHUO Li^{1, 2
,},
SUN Liangliang^{1, 2},
ZHANG Hui^{1, 2},
LI Xiaoguang^{1, 2},
YANG Yang^{3, 4},
WEI Wei^{3, 4}

1.
Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing 100124, China
2.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
3.
Department of Gastroenterology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
4.
Beijing Key Laboratory of Traditional Chinese Medicine Treatment of Functional Gastrointestinal Diseases, Beijing 100102, China

Funds: This work was supported by the National Natural Science Foundation of China (61871006), and Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (ZYYCXTD-C-202210)

More Information

Author Bio:
Yanping LI is an M.S. degree candidate at the Faculty of Information Technology, Beijing University of Technology (BUT), Beijing, China. She is also with the Beijing Key Laboratory of Computational Intelligence and Intelligent System, BUT. Her research interest is medical image processing and analysis. (Email: ypinglee@163.com)

Li ZHUO (corresponding author) received the Ph.D. degree in pattern recognition and intelligent system from BUT in 2004. She is currently a Professor with the Faculty of Information Technology, BUT. She is also with the Beijing Key Laboratory of Computational Intelligence and Intelligent System, BUT. She authored or coauthored more than 180 refereed journal and conference papers and has written six book chapters. Her current research interests include image/video processing, image recognition, and medical image processing. (Email: zhuoli@bjut.edu.cn)

Liangliang SUN is an M.S. degree candidate at the Faculty of Information Technology, Beijing University of Technology, Beijing, China. He is also with the Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology. His research interest is medical image processing and analysis

Hui ZHANG was born in 1982. He received the Ph.D. degree in signal and information processing from Beijing Institute of Technology, China in 2010. He is a Lecturer of electronic science and technology in Beijing University of Technology. His research interests include motion analysis and video processing

Xiaoguang LI received the B.S. degree in electronic information engineering and the Ph.D. degree in circuits and systems from BUT, in 2003 and 2008, respectively. He is currently an Associate Professor with BUT. He was a Research Associate with The Hong Kong Polytechnic University in 2009. He was a Visiting Scholar with The University of Sydney in 2012, and with the University of Southern California from 2017 to 2018. His current research interests include image processing and medical image analysis
Received Date: 2022-03-10
Accepted Date: 2022-06-21

Available Online: 2022-10-09

Publish Date: 2023-01-05

Abstract

Abstract

Tongue color is an important tongue diagnostic index for traditional Chinese medicine (TCM). Due to the individual experience of TCM experts as well as ambiguous boundaries among the tongue color categories, there often exist noisy labels in annotated samples. Deep neural networks trained with the noisy labeled samples often have poor generalization capability because they easily overfit on noisy labels. A novel framework named confident-learning-assisted knowledge distillation (CLA-KD) is proposed for tongue color classification with noisy labels. In this framework, the teacher network plays two important roles. On the one hand, it performs confident learning to identify, cleanse and correct noisy labels. On the other hand, it learns the knowledge from the clean labels, which will then be transferred to the student network to guide its training. Moreover, we elaborately design a teacher network in an ensemble manner, named E-CA²-ResNet18, to solve the unreliability and instability problem resulted from the insufficient data samples. E-CA²-ResNet18 adopts ResNet18 as the backbone, and integrates channel attention (CA) mechanism and activate or not activation function together, which facilitates to yield a better performance. The experimental results on three self-established TCM tongue datasets demonstrate that, our proposed CLA-KD can obtain a superior classification accuracy and good robustness with a lower network model complexity, reaching 94.49%, 92.21%, 93.43% on the three tongue image datasets, respectively.
- Traditional Chinese medicine,
- Tongue color classification,
- Confident learning,
- Learning from noisy labels,
- Knowledge distillation,
- Channel attention mechanism,
- ResNet18

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References(38)

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