Deep Survival Analysis With Latent Clustering and Contrastive Learning
文献类型：期刊论文
| 作者 | Cui, Chang1,2 ; Tang, Yongqiang1; Zhang, Wensheng1,2
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| 刊名 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
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| 出版日期 | 2024-05-01 |
| 卷号 | 28期号:5页码:3090-3101 |
| 关键词 | Analytical models Task analysis Self-supervised learning Predictive models Hazards Data models Correlation Survival analysis deep learning clustering contrastive learning survival prediction |
| ISSN号 | 2168-2194 |
| DOI | 10.1109/JBHI.2024.3362850 |
| 通讯作者 | Tang, Yongqiang(yongqiang.tang@ia.ac.cn) ; Zhang, Wensheng(zhangwenshengia@hotmail.com) |
| 英文摘要 | Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in survival analysis tasks. Although some progress has been made on this issue recently, the present methods generally treat the instances as separate ones while ignoring their potential correlations, thus rendering unsatisfactory performance. In this study, we propose a novel Deep Survival Analysis model with latent Clustering and Contrastive learning (DSACC). Specifically, we jointly optimize representation learning, latent clustering and survival prediction in a unified framework. In this way, the clusters distribution structure in latent representation space is revealed, and meanwhile the structure of the clusters is well incorporated to improve the ability of survival prediction. Besides, by virtue of the learned clusters, we further propose a contrastive loss function, where the uncensored data in each cluster are set as anchors, and the censored data are treated as positive/negative sample pairs according to whether they belong to the same cluster or not. This design enables the censored data to make full use of the supervision information of the uncensored samples. Through extensive experiments on four popular clinical datasets, we demonstrate that our proposed DSACC achieves advanced performance in terms of both C-index (0.6722, 0.6793, 0.6350, and 0.7943) and Integrated Brier Score (IBS) (0.1616, 0.1826, 0.2028, and 0.1120). |
| WOS关键词 | CENSORED-DATA ; MODEL ; REGRESSION ; SUBPOPULATIONS ; PREDICTION ; PROGNOSIS |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:001221547700015 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/58444]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Tang, Yongqiang; Zhang, Wensheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cui, Chang,Tang, Yongqiang,Zhang, Wensheng. Deep Survival Analysis With Latent Clustering and Contrastive Learning[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2024,28(5):3090-3101. |
| APA | Cui, Chang,Tang, Yongqiang,&Zhang, Wensheng.(2024).Deep Survival Analysis With Latent Clustering and Contrastive Learning.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,28(5),3090-3101. |
| MLA | Cui, Chang,et al."Deep Survival Analysis With Latent Clustering and Contrastive Learning".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 28.5(2024):3090-3101. |
入库方式： OAI收割
来源：自动化研究所
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