基于噪声样本渐近修正的中医舌色分类方法

孙亮亮; 李艳萍; 张辉; 卓力

doi:10.12263/DZXB.20220742

您当前的位置：

首页 >

文章列表页 >

基于噪声样本渐近修正的中医舌色分类方法

学术论文 | 更新时间：2025-12-11

- 基于噪声样本渐近修正的中医舌色分类方法
- A TCM Tongue Color Classification Method via Progressively Correcting Noisy Samples
- 电子学报 2024年52卷第5期页码：1450-1459
- 作者机构：
  
  1.北京工业大学信息学部，北京100124
  2.北京工业大学计算智能与智能系统北京重点实验室，北京100124
- 作者简介：
  
  [ "孙亮亮男，1996年11月出生于河南省周口市.现为北京工业大学信息学部2022届工程硕士研究生.主要研究方向为机器学习和中医信息化处理.E-mail: 18845634374@163.com" ]
  [ "李艳萍女，1997年4月出生于黑龙江省五大连池市.现为北京工业大学信息学部2023届工学硕士研究生.硕士期间获优秀毕业生、科技创新奖一等奖等奖项6项.主要研究方向为机器学习和中医信息化处理.E-mail: ypinglee@163.com" ]
  [ "张辉男.北京工业大学信息学部电子科学与技术学院（微电子学院）副教授、硕士生导师.毕业于北京理工大学信号与信息处理专业，获工学博士学位.主要从事视觉信息处理、人工智能系统设计与集成研究.E-mail: huizhang@bjut.edu.cn" ]
  [ "卓力女，1971年10月出生于江苏省徐州市.现为信息学部电子科学与技术学院人工智能与视觉大数据研究所所长、教授、博士生导师.获得教育部高等学校科学研究优秀成果奖（科学技术）技术发明奖二等奖和北京市科学技术奖三等奖各1项（均排名第一）、IEEE最佳论文奖等.在国内外发表学术论文200余篇.E-mail: zhuoli@bjut.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61871006);国家中医药管理局中医药创新团队及人才支持计划项目(ZYYCXTD-C-202210)
- DOI：10.12263/DZXB.20220742
  中图分类号： TP391;
- 收稿：2022-06-28，
  
  修回：2023-07-16，
  
  纸质出版：2024-05-25
- 稿件说明：
移动端阅览
孙亮亮,李艳萍,张辉,等. 基于噪声样本渐近修正的中医舌色分类方法[J]. 电子学报,2024,52(05):1450-1459.

SUN Liang-liang, LI Yan-ping, ZHANG Hui, et al. A TCM Tongue Color Classification Method via Progressively Correcting Noisy Samples[J]. Acta Electronica Sinica, 2024, 52(05): 1450-1459.
孙亮亮,李艳萍,张辉,等. 基于噪声样本渐近修正的中医舌色分类方法[J]. 电子学报,2024,52(05):1450-1459. DOI：10.12263/DZXB.20220742

SUN Liang-liang, LI Yan-ping, ZHANG Hui, et al. A TCM Tongue Color Classification Method via Progressively Correcting Noisy Samples[J]. Acta Electronica Sinica, 2024, 52(05): 1450-1459. DOI：10.12263/DZXB.20220742

摘要

基于深度学习的中医舌色分类模型具备良好的性能，但是依赖大量正确标注的样本.由于人工标注样本费时费力，不可避免地存在错误标注，导致模型在训练过程中对噪声样本过拟合，使其泛化能力变差.为此，本文提出了一种基于噪声样本渐近修正的中医舌色分类方法.首先，根据舌色分类的特点，提出了一种全局-局部特征融合方法，将其嵌入到ResNet18骨干网络中，构建了舌色分类网络，并采用集成学习范式，提高分类模型的可靠性和稳定性；其次，针对噪声样本下的舌色分类网络训练问题，提出了样本注意力机制和噪声样本标签重新标注机制，在训练过程中对干净样本和噪声样本加以区分，赋予不同的权重，并逐步对噪声样本标签进行修正；最后，采用Boostrapping损失函数降低模型对噪声样本的关注度，抑制噪声样本对分类性能的影响.将提出的方法在两个自建的舌色分类数据集上进行了实验验证，结果表明，该方法通过渐进地对噪声标签进行校正，可以获得比现有的有噪样本下图像分类方法更高的分类精度，Acc指标分别达到了94.6%和93.65%.

Abstract

Auto tongue color classification is an important research topic in the study of TCM (Traditional Chinese Medicine) objectification. Affected by various factors such as doctor’s experience and illumination conditions

there often exist errors in the manually annotated labels

that is

noisy labels. Noisy labels will cause the model not to converge in the training process and the generalization ability will be poor. Therefore

in this paper

a TCM tongue color classification method is proposed by progressively correcting noisy samples. First

according to the characteristics of the tongue color classification

a global-local feature fusion method is proposed

which is embedded in the ResNet18 backbone network

constructing a tongue color classification network. The ensemble learning paradigm is adopted to improve the reliability and stability of the classification model. Next

for the classification network training problem under noisy samples

a sample attention mechanism and a re-labeling mechanism are proposed. During the training process

different weights are assigned to clean samples and noisy samples

and the noisy samples are gradually adjusted. Finally

the network model is optimized and trained with the Boostrapping loss function to suppress the impact of noisy samples on the classification performance. The experimental results on two tongue color classification datasets SIPL-A and SIPL-B show that

the proposed method can effectively correct noisy labels

thereby

significantly improving the tongue color classification accuracy. Compared with the existing image classification methods under noisy samples

the proposed method can achieve a higher classification accuracy

reaching 94.6% and 93.65%

respectively.

关键词

Keywords

references

汤一平 , 王丽冉 , 何霞 , 等 . 基于多任务卷积神经网络的舌象分类研究 [J ] . 计算机科学 , 2018 , 45 ( 12 ): 255 - 261 .

TANG Y P , WANG L R , HE X , et al . Classification of tongue image based on multi-task deep convolutional neural network [J ] . Computer Science , 2018 , 45 ( 12 ): 255 - 261 . (in Chinese)

HOU J , SU H Y , YAN B , et al . Classification of tongue color based on CNN [C ] // 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA) . Piscataway : IEEE , 2017 : 725 - 729 .

FU S Y , ZHENG H , YANG Z J , et al . Computerized tongue coating nature diagnosis using convolutional neural network [C ] // 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA) . Piscataway : IEEE , 2017 : 730 - 734 .

肖庆新 . 基于深度学习的中医舌象特征分类技术研究 [D ] . 北京 : 北京工业大学 , 2019 .

XIAO Q X . Research on Classification Technology of Tongue Picture Features in Traditional Chinese Medicine Based on Deep Learning [D ] . Beijing : Beijing University of Technology , 2019 . (in Chinese)

ZHANG Z L , SABUNCU M R . Generalized cross entropy loss for training deep neural networks with noisy labels [C ] // Proceedings of the 32nd International Conference on Neural Information Processing Systems . New York : ACM , 2018 : 8792 - 8802 .

WANG Y S , MA X J , CHEN Z Y , et al . Symmetric cross entropy for robust learning with noisy labels [C ] // 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway : IEEE , 2019 : 322 - 330 .

LIN T Y , GOYAL P , GIRSHICK R , et al . Focal loss for dense object detection [C ] // 2017 IEEE International Conference on Computer Vision (ICCV) . Piscataway : IEEE , 2017 : 2999 - 3007 .

LYU Y M , TSANG I W . Curriculum loss: Robust learning and generalization against label corruption [EB/OL ] . ( 2019 )[2022 ] . http://arxiv.org/abs/1905.10045.pdf http://arxiv.org/abs/1905.10045.pdf .

MENON A , RAWAT A , REDDI S J , et al . Can gradient clipping mitigate label noise? [C/OL ] // International Conference on Learning Representations . 2020 [ 2022 ] . https://openreview.net/pdf?id=rklB76EKPr https://openreview.net/pdf?id=rklB76EKPr .

GOLDBERGER J , BEN-REUVEN E . Training deep neural-networks using a noise adaptation layer [C/OL ] // International Conference on Learning Representations . 2016 [ 2022 ] . https://openreview.net/pdf?id=H12GRgcxg https://openreview.net/pdf?id=H12GRgcxg .

CHENG L L , ZHOU X Z , ZHAO L M , et al . Weakly supervised learning with side information for noisy labeled images [M ] // Computer Vision - ECCV 2020 . Cham : Springer International Publishing , 2020 : 306 - 321 .

XIA X B , LIU T L , HAN B , et al . Extended T: Learning with mixed closed-set and open-set noisy labels [EB/OL ] . ( 2020 )[2022 ] . http://arxiv.org/abs/2012.00932.pdf http://arxiv.org/abs/2012.00932.pdf .

BEKKER A J , GOLDBERGER J . Training deep neural-networks based on unreliable labels [C ] // 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . Piscataway : IEEE , 2016 : 2682 - 2686 .

JINDAL I , NOKLEBY M , CHEN X W . Learning deep networks from noisy labels with dropout regularization [C ] // 2016 IEEE 16th International Conference on Data Mining (ICDM) . Piscataway : IEEE , 2016 : 967 - 972 .

HAN B , YAO J C , NIU G , et al . Masking: A new perspective of noisy supervision [C ] // Proceedings of the 32nd International Conference on Neural Information Processing Systems . New York : ACM , 2018 : 5841 - 5851 .

YAO J C , WANG J J , TSANG I W , et al . Deep learning from noisy image labels with quality embedding [J ] . IEEE Transactions on Image Processing , 2018 , 28 ( 4 ): 1909 - 1922 .

XIA X B , LIU T L , HAN B , et al . Robust early-learning: Hindering the memorization of noisy labels [C/OL ] // International Conference on Learning Representations . 2020 [ 2022 ] . https://openreview.net/pdf?id=Eql5b1_hTE4 https://openreview.net/pdf?id=Eql5b1_hTE4 .

NORTHCUTT C , JIANG L , CHUANG I . Confident learning: Estimating uncertainty in dataset labels [J ] . Journal of Artificial Intelligence Research , 2021 , 70 : 1373 - 1411 .

HAN B , YAO Q M , YU X R , et al . Co-teaching: Robust training of deep neural networks with extremely noisy labels [C ] // Proceedings of the 32nd International Conference on Neural Information Processing Systems . New York : ACM , 2018 : 8536 - 8546 .

YU X R , HAN B , YAO J C , et al . How does disagreement help generalization against label corruption? [EB/OL ] . ( 2019 )[2022 ] . http://arxiv.org/abs/1901.04215.pdf http://arxiv.org/abs/1901.04215.pdf .

HUANG J C , QU L , JIA R F , et al . O2U-net: A simple noisy label detection approach for deep neural networks [C ] // 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway : IEEE , 2019 : 3325 - 3333 .

SONG H , KIM M , PARK D , et al . Robust learning by self-transition for handling noisy labels [C ] // Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . New York : ACM , 2021 : 1490 - 1500 .

WU P X , ZHENG S Z , GOSWAMI M , et al . A topological filter for learning with label noise [C ] // Proceedings of the 34th International Conference on Neural Information Processing Systems . New York : ACM , 2020 : 21382 - 21393 .

REN M Y , ZENG W Y , YANG B , et al . Learning to reweight examples for robust deep learning [EB/OL ] . ( 2018 )[2022 ] . http://arxiv.org/abs/1803.09050.pdf http://arxiv.org/abs/1803.09050.pdf .

NGUYEN D T , MUMMADI C K , NGO T P N , et al . SELF: Learning to filter noisy labels with self-ensembling [EB/OL ] . ( 2019 )[2022 ] . http://arxiv.org/abs/1910.01842.pdf http://arxiv.org/abs/1910.01842.pdf .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2016 : 770 - 778 .

REED S , LEE H , ANGUELOV D , et al . Training deep neural networks on noisy labels with bootstrapping [J ] . Computer Science , 2014 , 14 ( 20 ): 687 - 698 .

MA N N , ZHANG X Y , ZHENG H T , et al . ShuffleNet V2: Practical guidelines for efficient CNN architecture design [C ] //Proceedings of Computer Vision - ECCV 2018: 15th European Conference , Part XIV . New York : ACM , 2018 : 122 - 138 .

SANDLER M , HOWARD A , ZHU M L , et al . MobileNetV2: Inverted residuals and linear bottlenecks [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 4510 - 4520 .

卓力 , 孙亮亮 , 张辉 , 等 . 有噪声标注情况下的中医舌色分类方法 [J ] . 电子与信息学报 , 2022 , 44 ( 1 ): 89 - 98 .

ZHUO L , SUN L L , ZHANG H , et al . TCM tongue color classification method under noisy labeling [J ] . Journal of Electronics & Information Technology , 2022 , 44 ( 1 ): 89 - 98 . (in Chinese)

孙亮亮 . 有噪声标注下的中医舌色分类研究 [D ] . 北京 : 北京工业大学 , 2022 .

SUN L L . Study on Classification of Tongue Color in Traditional Chinese Medicine with Noisy Labeling [D ] . Beijing : Beijing University of Technology , 2022 . (in Chinese)

SZEGEDY C , VANHOUCKE V , IOFFE S , et al . Rethinking the inception architecture for computer vision [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2016 : 2818 - 2826 .

YI K , WU J X . Probabilistic end-to-end noise correction for learning with noisy labels [C ] // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 7010 - 7018 .

PENG X J , WANG K , ZENG Z Y , et al . Suppressing mislabeled data via grouping and self-attention [C ] // Computer Vision - ECCV 2020 . Cham : Springer International Publishing , 2020 : 786 - 802 .

KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [J ] . Communications of the ACM , 2012 , 60 ( 6 ): 84 - 90 .

SIMONYAN K , ZISSERMAN A . Very deep convolutional networks for large-scale image recognition [J ] . Computer Science , 2014 , 14 ( 5 ): 1409 - 1556 .

HUANG G , LIU Z , VAN DER MAATEN L , et al . Densely connected convolutional networks [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2017 : 2261 - 2269 .

XIE S N , GIRSHICK R , DOLLÁR P , et al . Aggregated residual transformations for deep neural networks [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2017 : 5987 - 5995 .

TAN M X , PANG R M , LE Q V . EfficientDet: Scalable and efficient object detection [C ] // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 10778 - 10787 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据