TCM Tongue Color Classification Method under Noisy Labeling
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摘要: 舌色是中医(TCM)望诊最关注的诊察特征之一，自动准确的舌色分类是舌诊客观化研究的重要内容。由于不同类别舌色之间的视觉界限存在模糊性以及医生标注者的主观性等，标注的舌象数据中常含有噪声，影响舌色分类模型的训练。为此，该文提出一种有噪声标注情况下的中医舌色分类方法：首先，提出一种两阶段的数据清洗方法，对含有噪声的标注样本进行识别，并进行清洗；其次，设计一种基于通道注意力机制的轻型卷积神经网络，通过增强特征的表达能力，实现舌色的准确分类；最后，提出一种带有噪声样本过滤机制的知识蒸馏策略，该策略中加入了由教师网络主导的噪声样本过滤机制，进一步剔除噪声样本，同时利用教师网络指导轻型卷积神经网络的训练，提升了分类性能。在自建的中医舌色分类数据集上的实验结果表明，该文提出的舌色分类方法能以较低的计算复杂度，显著提升分类的准确率，达到了93.88%。Abstract: Tongue color is one of the most concerned diagnostic features of tongue diagnosis in Traditional Chinese Medicine (TCM). Automatic and accurate tongue color classification is an important content of the objectification of tongue diagnosis. Due to the vagueness of the visual boundaries between different types of tongue colors and the subjectivity of the doctors, the annotated tongue image data samples often contain noises, which has a negative effect on the training of the tongue color classification model. Therefore, in this paper, a tongue color classification method in TCM with noisy labels is proposed. Firstly, a two-stage data cleaning method is proposed to identify and clean noisy labeled samples. Secondly, a lightweight Convolutional Neural Network (CNN) based on the channel attention mechanism is designed in this paper to achieve accurate classification of tongue color by enhancing the expressiveness of features. Finally, a knowledge distillation strategy with a noise sample filtering mechanism is proposed. This strategy adds a noise sample filtering mechanism led by the teacher network to eliminate further noise samples. At the same time, the teacher network is used to guide the training of the light convolutional neural network to improve the classification performance.The experimental results on the self-established TCM tongue color classification dataset show that the proposed method in this paper can significantly improve the classification accuracy with lower computational complexity, reaching 93.88%.
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表 1 有噪声标准舌图像的数据清洗流程
输入： 原始数据集${D_o} = \{ ({x_1},{y_1}),({x_2},{y_2}) \cdots ({x_n},{y_n})\}$； 准确率阈值${\text{Ac} }{ {\text{c} }_{{\rm{th}}} } = 0.9$； 变量$ a = 0 $； 过程： 第1阶段： (1) 将${D_{\rm{o}}}$按照4:1随机划分为训练集和测试集； ${D_{ {\text{train} } } } = \{ ({x_{r + 1} },{y_{r + 1} }),({x_{r + 2} },{y_{r + 2} }), \cdots, ({x_{r + {\text{len} }({D_{ {\text{train} } } })} },{y_{r + {\text{len} }({D_{ {\text{train} } } })} })\}$ ${D_{ {\text{test} } } } = \{ ({x_{e + 1} },{y_{e + 1} }),({x_{e + 2} },{y_{e + 2} }) ,\cdots , ({x_{e + {\text{len} }({D_{ {\text{test} } } })} },{y_{e + {\text{len} }({D_{ {\text{test} } } })} })\}$ (2) 用$ {D_{{\text{train}}}} $训练ResNet18网络； (3) 用训练好的ResNet18模型对$ {D_{{\text{test}}}} $中样本进行预测得到
$\hat y = ({\hat y_{e + 1} }\; \cdots \;{\hat y_{e + {\text{len} }({D_{ {\text{test} } } })} })$；(4) for i in range ($ {\text{len}}({D_{{\text{test}}}}) $): if $ {\hat y_{e + i}}! = y{}_{e + i} $: $ {D_{{\text{test}}}} $.remove($ {x_{e + i}} $) ； $ {D_{{\text{detele}}}} $add($ {x_{e + i}} $) ； // 将删除的数据放在一起组成$ {D_{{\text{detele}}}} $ $ {\text{Acc}} = {\text{len}}({D_{{\text{test}}}})/({\text{len}}({D_{{\text{test}}}}) + {\text{len}}({D_{{\text{detele}}}}) - a) $； $ a = a + {\text{len}}({D_{{\text{detele}}}}) $； (5) while ${\text{Acc} } \le {\text{Ac} }{ {\text{c} }_{ {\text{th} } } }$ do $ {D_{{\text{onew}}}} \leftarrow {D_{{\text{train}}}} + {D_{{\text{test}}}} $；// 将去除噪声样本的
$ {D_{{\text{train}}}} $与$ {D_{{\text{test}}}} $组合形成$ {D_{{\text{onew}}}} $将步骤(1)中的${D_{\rm{o}}}$换成$ {D_{{\text{onew}}}} $，repeat 步骤(1)—步骤
(4)操作；end while 第2阶段： (6) 将最终的$ {D_{{\text{onew}}}} $训练ResNet18和VGG16； (7) 利用各个网络训练的最优网络模型对$ {D_{{\text{detele}}}} $中的样本进行
预测得到：$ {\hat y_r} = ({\hat y_{r + 1}},{\hat y_{r + 2}}, \cdots ,{\hat y_{r + {\text{len}}({D_{{\text{detele}}}})}}) $ // ResNet18模
型的预测值$ {\hat y_v} = ({\hat y_{v + 1}},{\hat y_{v + 2}}, \cdots ,{\hat y_{v + {\text{len}}({D_{{\text{detele}}}})}}) $ // VGG16模型
预测值for i in range ($ {\text{len}}({D_{{\text{detele}}}}) $): if $ {\hat y_{r + i}} = = {\hat y_{v + i}} $: // 当三者一致时，将预测结果作
为样本新的标注$ {y_i} = {\hat y_{v + i}} $ $ {D_{{\text{onew}}}} $.add($ {x_{r + i}} $) 输出：经过清洗后的舌象数据$ {D_{{\text{onew}}}} $ 
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表 2 噪声样本过滤流程
输入：训练集$ {D_t} = \{ ({x_1},{y_1}),({x_2},{y_2}), \cdots ,({x_n},{y_n})\} $； 教师网络$ {\text{Mode}}{{\text{l}}_t} $和已经训练好的模型参数$ \theta $以及损失函数
Loss；remember_rate=0.95； 单次送入网络的样本数量为batch_size； 过程： (1) 将训练集中的样本按照batch_size大小送入教师网络中； (2) 利用教师网络$ {\text{Mode}}{{\text{l}}_t} $和训练好的模型参数$ \theta $得到batch_size个
样本的预测结果；(3) 利用损失函数Loss计算样本的损失值，将batch_size个样本
的损失值按照升序方式排序；(4) 设置remember_rate将样本按照排序的结果保留前
batch_size×remember_rate个样本，清除掉剩余的样本；(5) 将步骤(4)保留的样本更新送入学生网络样本。 输出：干净的数据样本，并将数据送入学生网络
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表 5 采用知识蒸馏前后的对比实验结果
教师网络 学生网络 名称 精度 参数量(MB) 名称 精度 参数量(MB) × × × 轻型卷积
神经网络0.9287 5.2 ResNet18+CBAM 0.9418 11.8 0.9345 ResNet50 0.9415 25.6 0.9356 ResNext50 0.9423 25.1 0.9345 ResNet101 0.9435 44.6 0.9352 ResNet50+SeNet 0.9447 28.1 0.9388
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表 6 不同分类网络的比较结果(%)
淡红舌 红舌 暗红舌 紫舌 整体精度 DenseNet121 82.60 68.00 84.61 88.89 82.92 ResNet18 75.00 76.92 91.30 88.89 82.34 ResNext50 78.26 84.00 80.76 77.78 80.72 ShuffleNetV2 86.95 76.00 76.92 88.89 81.14 MobileNetV2 86.95 80.00 76.92 77.78 81.32 EfficientNet-b4 86.95 64.00 84.61 77.78 81.45 本文方法 91.30 92.00 96.15 100.00 93.88
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