利用低信噪比小样本太赫兹光谱实现心肌淀粉样变检测

郑江鹏; 余平; 赵萌; 石凡; 孙续国; 陈胜勇

doi:10.37188/CO.2021-0223

利用低信噪比小样本太赫兹光谱实现心肌淀粉样变检测

doi: 10.37188/CO.2021-0223

郑江鹏^1,,
余平^1,,
赵萌^1, ,,
石凡^1, ,,
孙续国^2,,
陈胜勇^1,

1.
学习型智能系统教育部工程研究中心, 天津理工大学计算机科学与工程学院, 天津 300384
2.
天津医科大学医学检验学院, 天津 300203

基金项目: 国家自然科学基金资助项目（No. 61906133, No. 62020106004, No. 92048301, No. 61703304, No. 61906134, No. 61903275, No.61902078）

详细信息

作者简介:
郑江鹏（1997—），男，浙江温州人，硕士研究生，2020年于江西农业大学获得工学学士学位，现为天津理工大学计算机科学与工程学院硕士研究生，主要研究领域为光谱视觉，计算机视觉。E-mail：zjp_1997@stud.tjut.edu.cn

余平（1998—），女，河南信阳人，硕士研究生，2020年于天津财经大学获得工学学士学位，现为天津理工大学计算机科学与工程学院硕士研究生，主要研究领域为光谱视觉，计算机视觉。E-mail：pingyucv@stud.tjut.edu.cn

赵　萌（1988—），女，河北保定人，博士，副教授，硕士生导师，2016年于天津大学获得博士学位，现为天津理工大学讲师，主要研究领域为医学图像，机器视觉。E-mail：zh_m@tju.edu.cn

石　凡（1984—），男，河北保定人，博士，副教授，硕士生导师，2012年于南开大学获得博士学位，现为天津理工大学副教授，主要研究领域为机器视觉，模式识别，光学。E-mail：shifan@email.tjut.edu.cn

孙续国（1965—），男,河北唐山人，教授，博士生导师，2006年于日本熊本大学医学院获得博士学位，主要研究领域为临床医学检查方法学，细胞病理学人工智能检测方法学，其是天津检验和测试技术部产业联盟主席。E-mail：sunxuguo@tmu.edu.cn

陈胜勇（1973—），男，浙江温州人，博士，教授，博士生导师，2003年于香港大学获得计算机视觉博士学位，主要研究领域为计算机视觉，机器人技术，图像分析。他于2006至2007年间在汉堡大学工作。他在国际期刊上发表了100多篇科学论文。研究兴趣包括计算机视觉、机器人学和图像分析。陈博士是IET研究员和CCF高级成员，2013年中国杰出青年基金获得者。E-mail：sy@ieee.org

中图分类号: TP394.1;TH691.9
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出版历程
- 收稿日期: 2021-12-16
- 修回日期: 2022-01-12
- 录用日期: 2022-01-28
- 网络出版日期: 2022-04-13
- 刊出日期: 2022-05-20

Detection of myocardial amyloidosis by a small number of terahertz spectra with low signal-to-noise ratio

ZHENG Jiang-peng^1
,,
YU Ping^1
,,
ZHAO Meng^{1
, ,},
SHI Fan^{1
, ,},
SUN Xu-guo^2
,,
CHEN Sheng-yong^1
,

1.
Learning Intelligent System Engineering Research Center of the Ministry of Education, School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384
2.
School of Medical Laboratory, Tianjin Medical University, Tianjin 300203

Funds: Supported by National Natural Science Foundation of China (No. 61906133, No. 62020106004, No. 92048301, No. 61703304, No. 61906134, No. 61903275, No. 61902078)

More Information

Corresponding author: zh_m@tju.edu.cn; shifan@email.tjut.edu.cn

摘要

摘要: 由于低信噪比的小样本太赫兹光谱的可区分性特征提取困难和样本量过少带来的深度学习模型自身的过拟合问题，将太赫兹光谱与深度学习相结合应用于心肌淀粉样变检测仍面临挑战。本文提出了一种基于多模块顺序级联的分类模型，用于心肌淀粉样变在算法层面的实时检测。首先，采集了少量的低信噪比太赫兹光谱并对其进行预处理。其次，构建了一个基于卷积降噪自编码器、多尺度特征提取模块、密集连接模块的深度学习模型。最后，通过五折交叉验证策略进行病变预测，以获得稳定、可靠的结果。 10次独立重复实验和对比实验结果表明，该方法能对含噪光谱进行准确、稳定的分类，且其综合指标更优。不同样本量下的实验表明，本方法对样本量变化具有适应性：数据量为100时可达到95%的准确率；数据量仅为20时，该模型仍能取得70%的准确率。该项工作对心肌淀粉样变的实时、高效、安全诊断具有重要意义。
- 太赫兹光谱 /
- 心肌淀粉样变 /
- 低信噪比 /
- 小样本 /
- 深度学习
Abstract: Due to the difficulty of extracting the distinguishable features of a small number of terahertz spectra with low signal-to-noise ratio; second and the over fitting problem of the deep learning model itself caused by too few samples, the application of terahertz spectra and deep learning in myocardial amyloidosis detection exists some challenges. In this paper, we propose a classification model based on multi-modules sequential cascade for real-time detection of myocardial amyloidosis at the algorithm level. Firstly, we collect a small number of low SNR terahertz spectra and preprocess them. Secondly, we construct a deep learning model based on denoising autoencoder, multi-scale feature extraction module and dense connection module. Finally, we use the 5 folds cross validation strategy to predict the lesions to obtain stable and reliable results. The results of 10 times independent repeated experiment and comparative experiment show that this method can classify the spectra with noise accurately and stably, which possesses of a better performance. Experiments under different number of samples show that this method is adaptive to the change of dataset size: an accuracy of 95% is achieved corresponding to 100 samples; when the amount of samples is only 20, the model can still achieve an accuracy of 70%. Therefore, the proposed method is of great significance for the real-time, efficient and safe diagnosis of myocardial amyloidosis.
- terahertz spectroscopy /
- myocardial amyloidosis /
- low signal to noise ratio /
- a small number of samples /
- deep learning

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图 1 整体框架图

Figure 1. Overall framework of the proposed method

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图 2 参考信号的时域及频域光谱

Figure 2. Time-domain and frequency-domain spectra of the reference signal

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图 3 阴性、阳性样本的整体范围趋势图

Figure 3. Overall trends of negative and positive samples

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图 4 模型分类预测得到的混淆矩阵

Figure 4. Confusion matrix obtained by classification prediction of the model

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图 5 该模型的不同评价指标值

Figure 5. Evaluation indicators of the proposed model

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图 6 与15种机器学习算法和深度学习模型的对比实验

Figure 6. Comparative experiments of proposed model with 15 kinds of machine learning algorithms and deep learning models

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图 7 各模块的消融实验结果

Figure 7. Ablation experimental results of different modules

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表 1 10次独立重复实验结果

Table 1. Results of 10 times of independent repeated tests

实验序号	准确率（%）	精确度（%）	召回率（%）	F1分数（%）
1	95.00	100.00	91.66	95.65
2	95.00	100.00	92.30	95.99
3	94.85	99.76	92.32	95.89
4	94.93	99.86	91.75	95.63
5	95.04	100.00	92.28	95.98
6	94.66	99.80	92.25	95.87
7	95.12	99.75	91.84	95.63
8	95.06	99.66	92.25	95.81
9	94.75	99.62	92.32	95.83
10	94.84	99.82	91.75	95.61
极差（%）	0.46	0.38	0.66	0.38

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表 2 不同参数对模型分类效果的影响

Table 2. Effects of different parameters on model classification

学习率/ 批大小	准确率（%）	精确度（%）	召回率（%）	F1分数（%）
0.001/3	85.00	81.81	90.00	85.71
0.001/5	86.21	90.00	81.81	85.71
0.001/10	90.00	98.56	84.61	91.05
0.0001/3	85.00	90.90	83.33	86.95
0.0001/5	90.00	91.66	91.66	91.66
0.0001/10	95.00	100.00	92.30	95.99

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表 3 样本量对本文模型分类效果的影响

Table 3. Influence of the number of sample on model classification effect

样本量	准确率（%）	精确度（%）	召回率（%）	F1分数（%）
20	70.00	72.35	64.23	68.04
40	76.54	78.25	75.00	76.59
60	83.33	86.80	82.78	84.74
80	93.75	100.00	85.71	92.30
100	95.00	100.00	92.30	95.99

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表 4 扩增样本量对模型分类效果的影响

Table 4. Influence of the number of expanded samples on model classification effect

模型	准确率（%）	精确度（%）	召回率（%）	F1分数（%）
ResNet	64.57	60.31	64.59	62.37
DenseNet	63.66	65.87	68.30	67.06
CNN	64.83	66.05	65.66	65.85
本文模型	66.50	71.47	68.35	69.87

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