基于文本与图像的肺疾病研究与预测

吕晴; 赵奎; 曹吉龙; 魏景峰

doi:10.16383/j.aas.c190645

基于文本与图像的肺疾病研究与预测

doi: 10.16383/j.aas.c190645

吕晴^1,,
赵奎^1,,
曹吉龙^2,,
魏景峰^3,

1.
中国科学院沈阳计算技术研究所沈阳 110168
2.
中国医科大学附属第四医院沈阳 110032
3.
辽宁省医疗器械检验检测院沈阳 110000

基金项目: 国家水体污染控制与治理科技重大专项(2012ZX07505004)资助

详细信息

作者简介:
吕晴：中国科学院沈阳计算技术研究所硕士研究生. 2017年获得曲阜师范大学信息科学与工程专业学士学位. 主要研究方向为医学图像处理.E-mail: lvqing17@mails.ucas.ac.cn

赵奎：中国科学院沈阳计算技术研究所研究员. 2017年获得中国科学院大学硕士学位. 主要研究方向为人工智能, 大数据, 物联网. 本文通信作者. E-mail: zhaokui@sict.ac.cn

曹吉龙：中国医科大学附属第四医院信息中心主任. 2013年获得东北大学硕士学位. 主要研究方向为医疗信息化, 医疗健康物联网, 医疗信息安全.E-mail: jlcao@cmu.edu.cn

魏景峰：辽宁省医疗器械检验检测院高级工程师. 2011年获得中国医科大学生物医学工程专业硕士学位. 主要研究方向为源医疗器械检验, 电磁兼容检测, 检测实验室质量体系管理.E-mail: 13898154351@163.com

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出版历程
- 收稿日期: 2019-09-09
- 录用日期: 2020-01-28
- 网络出版日期: 2021-12-23
- 刊出日期: 2022-02-18

Research and Prediction of Lung Diseases Based on Text and Images

1.
Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168
2.
The Fourth Affiliated Hospital of China Medical University, Shenyang 110032
3.
Liaoning Provincial Medical Device Inspection and Testing Institute, Shenyang 110000

Funds: Supported by National Science and Technology Major Project of Water Pollution Control and Treatment (2012ZX07505004)

More Information

Author Bio:
LV Qing　Master student at Shenyang Institute of Computing Technology, Chinese Academy of Sciences. She received her bachelor degree in information science and engineering from Qufu Normal University in 2017. Her main research interest is medical image processing

ZHAO Kui　Professor at Shenyang Institute of Computing Technology, Chinese Academy of Sciences. He received his master degree from University of Chinese Academy of Sciences in 2017. His research interest covers artificial intelligence, big data, and the internet of things. Corresponding author of this paper

CAO Ji-Long　Director at the Information Center, the Fourth Affiliated Hospital of China Medical University. He received his master degree from Northeastern University in 2013. His research interest covers hospital information, health internet of things, and medical information security

WEI Jing-Feng　Senior engineer at Liaoning Medical Device Testi Institute. He received his master degree in biomedical engineering from China Medical University in 2011. His research interest covers medical electrical equipment test, electromagnetic compatibility test, and quality management of testing laboratories

摘要

摘要: 通过对目前现有的肺癌检测技术研究, 发现大部分研究人员主要针对肺癌(Computed tomography, CT)影像进行研究, 忽略了电子病历所隐藏的肺癌信息, 本文提出一种基于图像与文本相结合的肺癌分类方法, 从现有的基于深度学习的肺癌图像分类出发, 引入了电子病历信息, 使用Multi-head attention以及(Bi-directional long short-term memory, Bi-LSTM)对文本建模. 实验结果证明, 将电子病历信息引入到图像分类模型之后, 对模型的性能有进一步的提升. 相对仅使用电子病历进行预测, 准确率提升了大约14 %, 精确率大约提升了15 %, 召回率提升了14 %. 相对仅使用肺癌CT影像来进行预测, 准确率提升了3.2 %, 精确率提升了4 %, 召回率提升了4 %.
- 深度学习 /
- 神经网络 /
- 多头注意力机制 /
- bi-LSTM /
- 肺癌
Abstract: Through the study of the existing lung cancer detection technology, we found that most researchers mainly focus on the lung cancer (CT) images, ignoring the information of lung cancer hidden in the electronic medical records, this paper presents a lung cancer classification method based on the combination of image and text. Starting from the existing lung cancer image classification based on depth learning, the electronic medical record information is introduced, modeling text using Multi-head attention and (Bi-directional long short-term memory, BI-LSTM). The experimental results show that the performance of the image classification model is improved by introducing electronic medical record information. Predictions using only electronic medical records improved by about 14%, precision by about 15%, and recall by 14%. Compared to using only lung cancer CT images for prediction, the accuracy increased 3.2% , the precision increased 4% , and the recall increased 4%.
- Deep learning /
- neural network /
- multi-head attention /
- bi-LSTM /
- lung cancer

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图 1 模型结构图

Fig. 1 Model structure

下载: 全尺寸图片幻灯片

图 2 图像模型结构图

Fig. 2 Image model structure

下载: 全尺寸图片幻灯片

表 1 检验项目

Table 1 Examine items

	参考范围	检验名称	状态	结果值
血常规检查	0 ~ 0.1	嗜碱性粒细胞	正常	0.01
	0.05 ~ 0.5	嗜酸性粒细胞	正常	0.07
	0 ~ 1	嗜碱性粒细胞比率	正常	0.20 %
	110 ~ 160	血红蛋白	正常	128 g/L
	100 ~ 300	血小板	正常	$13510{\hat 9}/{\rm{L}}$
	3.5 ~ 5.5	红细胞	正常	4.25
	37 ~ 50	红细胞分布宽度	正常	43.90 %
	4 ~ 10	白细胞	正常	$6.1810{\hat 9}/{\rm{L}}$
	86 ~ 100	红细胞平均体积	正常	88.2 fL
痰液检查	无肿瘤细胞	痰液细胞	正常	无肿瘤细胞
肿瘤标记物	5 μg/ml	CEA (Carcinoembryonic antigen)	正常	2.31
	30 U/ml	CA125 (Cancer antigen 125)	正常	13.70 U/ml
	8.20 U/ml	CA72-4 (Cancer antigen 72-4)	正常	1.34 U/ml
	16.3 ng/ml	NSE (Neuron-specific enolase)	正常	15.18 ng/ml
	1.5 ng/ml	SCC (Squamous cell carcinoma)	正常	0.8 ng/ml
	2.0 ng/ml	CYFRA21-1 (Cytokeratin fragment 19)	高	7.31 ng/ml
胸水检验	0.38 ~ 2.1	甘油三脂	正常	0.74 mmol/L
	0.8 ~ 1.95	高密度脂蛋白	正常	1.31 mmol/L
	3.8 ~ 6.1	葡萄糖	高	10.11 mmol/L
	2 ~ 4	低密度脂蛋白	正常	2.02 mmol/L
	109 ~ 271	乳酸脱氢酶	正常	205.2 U/L
	0 ~ 6.8	直接胆红素	正常	3.49 μmol/L
	3.6 ~ 5.9	总胆固醇	低	3.54 mmol/L
	20 ~ 45	球蛋白	正常	31.7 g/L

下载: 导出CSV

表 2 MLP参数设置

Table 2 The parameter of MLP

Name	节点个数	激活函数
Hidden1	65	Sigmoid
Hidden2	131	Sigmoid
Hidden3	263	Sigmoid

下载: 导出CSV

表 3 正负样本比例

Table 3 Positive and negative sample ratio

正样本	1 262
负样本	2 523

下载: 导出CSV

表 4 实验1的结果

Table 4 The result of experiment 1

Model name	Train (%)			Test (%)
Model name	Accuracy	Precision	Recall	Accuracy	Precision	Recall
Text-net	83.12 ± 0.02	80.10 ± 0.05	81.12 ± 0.02	81.21 ± 0.01	79.82 ± 0.03	80.15 ± 0.01
Text-net1	76.87 ± 0.02	75.29 ± 0.01	75.11 ± 0.03	74.91 ± 0.02	73.41 ± 0.02	74.07 ± 0.03
Text-net2	80.49 ± 0.03	78.16 ± 0.04	78.82 ± 0.03	78.43 ± 0.02	77.15 ± 0.01	78.59 ± 0.02
Text-net3	79.73 ± 0.02	77.19 ± 0.02	76.92 ± 0.01	78.19 ± 0.02	76.79 ± 0.03	75.57 ± 0.02

下载: 导出CSV

表 5 实验2的结果

Table 5 The result of experiment 2

Model Name	Train (%)			Test (%)
Model Name	Accuracy	Precision	Recall	Accuracy	Precision	Recall
TI-Net	97.08 ± 0.03	95.69 ± 0.01	94.37 ± 0.02	96.90 ± 0.04	95.17 ± 0.03	93.71 ± 0.01
Img+MLP	95.15 ± 0.03	93.90 ± 0.02	93.17 ± 0.03	94.76 ± 0.02	92.89 ± 0.03	91.78 ± 0.01
Img+Text	94.71 ± 0.02	92.13 ± 0.03	91.26 ± 0.04	93.17 ± 0.04	90.88 ± 0.03	89.99 ± 0.03
MLP+Text	89.88 ± 0.04	87.67 ± 0.01	86.92 ± 0.02	87.78 ± 0.03	84.23 ± 0.03	84.57 ± 0.04
Img-Net	93.85 ± 0.03	91.84 ± 0.02	90.83 ± 0.03	92.67 ± 0.02	89.77 ± 0.03	88.93 ± 0.01
VGG-19	92.53 ± 0.02	89.16 ± 0.03	88.57 ± 0.01	90.94 ± 0.02	87.10 ± 0.03	87.04 ± 0.02
MLP	86.75 ± 0.03	85.21 ± 0.02	85.12 ± 0.03	84.86 ± 0.02	82.37 ± 0.03	81.59 ± 0.01
Text-Net	83.12 ± 0.04	80.10 ± 0.05	81.12 ± 0.02	81.21 ± 0.03	79.82 ± 0.03	80.15 ± 0.02

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