CASIA OpenIR  > 中国科学院分子影像重点实验室
A multi-view co-training network for semi-supervised medical image-based prognostic prediction
Li, Hailin1,2; Wang, Siwen2,3; Liu, Bo4,5; Fang, Mengjie1,2; Cao, Runnan2,3; He, Bingxi1,2; Liu, Shengyuan2,3; Hu, Chaoen2; Dong, Di2,3; Wang, Ximing5; Wang, Hexiang6; Tian, Jie1,2
Source PublicationNEURAL NETWORKS
ISSN0893-6080
2023-07-01
Volume164Pages:455-463
Corresponding AuthorDong, Di(di.dong@ia.ac.cn) ; Wang, Ximing(wxming369@163.com) ; Wang, Hexiang(wanghexiang@qdu.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractPrognostic prediction has long been a hotspot in disease analysis and management, and the de-velopment of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge in prognostic prediction is to model a regression problem based on censored observations, and semi-supervised learning has the potential to play an important role in improving the utilization efficiency of censored data. However, there are yet few effective semi-supervised paradigms to be applied. In this paper, we propose a semi -supervised co-training deep neural network incorporating a support vector regression layer for survival time estimation (Co-DeepSVS) that improves the efficiency in utilizing censored data for prognostic prediction. First, we introduce a support vector regression layer in deep neural networks to deal with censored data and directly predict survival time, and more importantly to calculate the labeling confidence of each case. Then, we apply a semi-supervised multi-view co-training framework to achieve accurate prognostic prediction, where labeling confidence estimation with prior knowledge of pseudo time is conducted for each view. Experimental results demonstrate that the proposed Co-DeepSVS has a promising prognostic ability and surpasses most widely used methods on a multi-phase CT dataset. Besides, the introduction of SVR layer makes the model more robust in the presence of follow-up bias. (c) 2023 Published by Elsevier Ltd.
KeywordDeep neural network Medical image analysis Prognostic prediction Semi-supervised learning
DOI10.1016/j.neunet.2023.04.030
WOS KeywordLYMPH-NODE METASTASIS ; REGRESSION ; CANCER ; MODELS ; CLASSIFICATION
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Sciences[XDB38040200] ; National Natural Science Foundation of China[82022036] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81971580] ; National Natural Science Foundation of China[91959205] ; National Natural Science Foundation of China[81971619] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81871354] ; National Key R&D Program of China[2017YFA0205200] ; Beijing Natural Science Foundation, China[Z20J00105] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Project of High-Level Talents Team Introduction in Zhuhai City, China[Zhuhai HLHPTP201703] ; Youth Innovation Promotion Association CAS, China[Y2021049] ; Taishan Scholars Project ; Academic Promotion Programme of Shandong First Medical University, China[2019QL023]
Funding OrganizationStrategic Priority Research Program of Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Key R&D Program of China ; Beijing Natural Science Foundation, China ; Chinese Academy of Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City, China ; Youth Innovation Promotion Association CAS, China ; Taishan Scholars Project ; Academic Promotion Programme of Shandong First Medical University, China
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000999170100001
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/53409
Collection中国科学院分子影像重点实验室
Corresponding AuthorDong, Di; Wang, Ximing; Wang, Hexiang; Tian, Jie
Affiliation1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Lanzhou Univ, Hosp 2, Lanzhou 730050, Gansu, Peoples R China
5.Shandong Univ, Shandong Med Univ 1, Shandong Prov Hosp, Dept Radiol, Jinan 250021, Shandong, Peoples R China
6.Qingdao Univ, Affiliated Hosp, Dept Radiol, Qingdao 266000, Shandong, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Hailin,Wang, Siwen,Liu, Bo,et al. A multi-view co-training network for semi-supervised medical image-based prognostic prediction[J]. NEURAL NETWORKS,2023,164:455-463.
APA Li, Hailin.,Wang, Siwen.,Liu, Bo.,Fang, Mengjie.,Cao, Runnan.,...&Tian, Jie.(2023).A multi-view co-training network for semi-supervised medical image-based prognostic prediction.NEURAL NETWORKS,164,455-463.
MLA Li, Hailin,et al."A multi-view co-training network for semi-supervised medical image-based prognostic prediction".NEURAL NETWORKS 164(2023):455-463.
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