基于生成对抗网络的肺结节良恶性诊断算法

doi:10.12068/j.issn.1005-3026.2022.01.004

东北大学学报（自然科学版） ›› 2022, Vol. 43 ›› Issue (1): 24-32.DOI: 10.12068/j.issn.1005-3026.2022.01.004

基于生成对抗网络的肺结节良恶性诊断算法

罗家健¹，冯宝²，陈相猛³，顾正晖¹

)(1. 华南理工大学自动化科学与工程学院，广东广州510640; 2. 桂林航天工业学院医学人工智能实验室，广西桂林541004; 3. 江门市中心医院放射科，广东江门529030)

修回日期:2021-06-09 接受日期:2021-06-09 发布日期:2022-01-25
通讯作者: 罗家健
作者简介:罗家健(1997-)，男，广东广州人，华南理工大学硕士研究生；顾正晖(1973-)，女，广东新兴人，华南理工大学教授.
基金资助:
国家自然科学基金资助项目(81960324， 61876064， 61967004)；广东省基础与应用基础研究项目(2019A1515011773).

Diagnosis Algorithm of Pulmonary Nodules Malignancy Based on Generative Adversarial Network

LUO Jia-jian¹， FENG Bao²， CHEN Xiang-meng³， GU Zheng-hui¹

1. School of Automation Science and Engineering， South China University of Technology， Guangzhou 510640， China; 2. Medical Artificial Intelligence Laboratory， Guilin University of Aerospace Technology， Guilin 541004， China; 3. The Department of Radiology， Jiangmen Central Hospital， Jiangmen 529030， China.

Revised:2021-06-09 Accepted:2021-06-09 Published:2022-01-25
Contact: FENG Bao
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对实性肺结节CT影像数据量少、人工标注耗时耗力等问题，提出一种结合生成对抗网络和集成学习的实性肺结节良恶性计算机辅助诊断方法.首先，使用基于梯度惩罚的生成对抗网络对肺结节CT影像数据集进行扩充，缓解由数据量少、样本类别不均衡导致的模型过拟合.然后，利用卷积神经网络进行CT影像特征提取，并通过主成分分析对深度特征进行降维.最后，联合CT图像特征和有效临床信息，采用集成学习方法构建分类模型预测实性肺结节良恶性.基于多中心临床数据分析表明，相比于传统卷积神经网络模型，所提出方法有更好的预测性能.

关键词: 实性肺结节;肺腺癌;计算机断层扫描;生成对抗网络;集成学习

Abstract: To solve the problem of data scarcity and expensive costs in manual labeling of CT images of solid pulmonary nodules， a computer aided diagnosis algorithm for classification between lung tuberculosis and lung adenocarcinoma of solid pulmonary nodules by combining generative adversarial network and ensemble learning was proposed. Firstly， the original CT image dataset was augmented using Wasserstein generative adversarial network with gradient penalty(WGAN-GP)， in order to relieve the problem of overfitting caused by small-scale dataset and class imbalance. Then， feature extraction was performed using convolutional neural network， followed by a dimension reduction procedure using principal component analysis(PCA). Finally， deep features concatenated with effective subjective features were classified by an ensemble learning model to give final prediction of the patient.Analysis based on multi-center clinical data indicated that the proposed algorithm has better performance compared to traditional convolutional neural network method.

Key words: solid pulmonary nodules; lung adenocarcinoma; computed tomography(CT); generative adversarial network(GAN); ensemble learning

中图分类号:

TP391.41

罗家健，冯宝，陈相猛，顾正晖. 基于生成对抗网络的肺结节良恶性诊断算法[J]. 东北大学学报（自然科学版）, 2022, 43(1): 24-32.

LUO Jia-jian， FENG Bao， CHEN Xiang-meng， GU Zheng-hui. Diagnosis Algorithm of Pulmonary Nodules Malignancy Based on Generative Adversarial Network[J]. Journal of Northeastern University(Natural Science), 2022, 43(1): 24-32.

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基于生成对抗网络的肺结节良恶性诊断算法

Diagnosis Algorithm of Pulmonary Nodules Malignancy Based on Generative Adversarial Network

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