Learning to learn by yourself: Unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases

doi:10.1002/int.22449

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Learning to learn by yourself: Unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases
	Zheng, Wenbo 1,2; Yan, Lan2,3 ; Gou, Chao4 ; Zhang, Zhi-Cheng 5; Zhang, Jun J.2,6; Hu, Ming 7; Wang, Fei-Yue2
发表期刊	INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
ISSN	0884-8173
	2021-05-13
页码	32
通讯作者	Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
摘要	The goal of diagnosing the coronavirus disease 2019 (COVID-19) from suspected pneumonia cases, that is, recognizing COVID-19 from chest X-ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep-learning-based methods. (2) Many public COVID-19 data sets contain only a few images without fine-grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID-19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID-19. To address these issues, we propose a novel framework called Unsupervised Meta-Learning with Self-Knowledge Distillation to address the problem of differentiating COVID-19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self-knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state-of-the-art methods. Moreover, we construct a new COVID-19 pneumonia data set based on text mining, consisting of 2696 COVID-19 images (347 X-ray + 2349 CT), 10,155 images (9661 X-ray + 494 CT) about other types of pneumonia, and the fine-grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.
关键词	biomedical imaging COVID‐ 19 knowledge distillation meta‐ learning unsupervised learning
DOI	10.1002/int.22449
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[61806198] ; National Natural Science Foundation of China[U1811463] ; Key Research and Development Program of Guangzhou[202007050002] ; National Key Research and Development Program[2018AAA0101502] ; National Key R&D Program of China[2020YFB1600400]
项目资助者	National Natural Science Foundation of China ; Key Research and Development Program of Guangzhou ; National Key Research and Development Program ; National Key R&D Program of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000649729200001
出版者	WILEY
七大方向——子方向分类	人工智能+医疗
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44685
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Wang, Fei-Yue
作者单位	1.Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China 5.Gen Hosp Peoples Liberat Army, Med Ctr 7, Beijing, Peoples R China 6.Wuhan Univ, Sch Elect Engn & Automat, Wuhan, Peoples R China 7.Wuhan Pulm Hosp, Intens Care Unit, Wuhan, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zheng, Wenbo,Yan, Lan,Gou, Chao,et al. Learning to learn by yourself: Unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases[J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,2021:32.
APA	Zheng, Wenbo.,Yan, Lan.,Gou, Chao.,Zhang, Zhi-Cheng.,Zhang, Jun J..,...&Wang, Fei-Yue.(2021).Learning to learn by yourself: Unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases.INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,32.
MLA	Zheng, Wenbo,et al."Learning to learn by yourself: Unsupervised meta-learning with self-knowledge distillation for COVID-19 diagnosis from pneumonia cases".INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2021):32.