Deep Contrastive Survival Analysis with Dual-View Clustering Article Swipe
C.Y. Cui
,
Yongqiang Tang
,
Wensheng Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13244866
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13244866
Survival analysis aims to analyze the relationship between covariates and events of interest, and is widely applied in multiple research fields, especially in clinical fields. Recently, some studies have attempted to discover potential sub-populations in survival data to assist in survival prediction with clustering. However, existing models that combine clustering with survival analysis face multiple challenges: incomplete representation caused by single-path encoders, the incomplete information of pseudo-samples, and misleading effects of boundary samples. To overcome these challenges, in this study, we propose a novel deep contrastive survival analysis model with dual-view clustering. Specifically, we design a Siamese autoencoder to construct latent spaces in two views and conduct dual-view clustering to more comprehensively capture patient representations. Moreover, we consider the dual views as mutual augmentations rather than introducing pseudo-samples and, based on this, triplet contrastive learning is proposed to fully utilize clustering information and dual-view representations to enhance survival prediction. Additionally, we employ a self-paced learning strategy in the dual-view clustering process to ensure the model handles samples from easy to hard in training, thereby avoiding the misleading effects of boundary samples. Our proposal achieves an average C-index and IBS of 0.6653 and 0.1786 on three widely used clinical datasets, both exceeding the existing best methods, which demonstrates its advanced discriminative and calibration performance.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13244866
- https://www.mdpi.com/2079-9292/13/24/4866/pdf?version=1733825978
- OA Status
- gold
- Cited By
- 1
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405239335
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Raw OpenAlex JSON
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https://openalex.org/W4405239335Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics13244866Digital Object Identifier
- Title
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Deep Contrastive Survival Analysis with Dual-View ClusteringWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-10Full publication date if available
- Authors
-
C.Y. Cui, Yongqiang Tang, Wensheng ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics13244866Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-9292/13/24/4866/pdf?version=1733825978Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-9292/13/24/4866/pdf?version=1733825978Direct OA link when available
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Cluster analysis, Dual (grammatical number), Artificial intelligence, Computer science, Contrastive analysis, Natural language processing, Pattern recognition (psychology), Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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55Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4391942283, https://openalex.org/W4390581326, https://openalex.org/W4403474955, https://openalex.org/W4379143884, https://openalex.org/W3158053269, https://openalex.org/W4387844595, https://openalex.org/W3147894994, https://openalex.org/W4401875620, https://openalex.org/W2026094606, https://openalex.org/W3010540599, https://openalex.org/W4213375977, https://openalex.org/W2882975375, https://openalex.org/W2753919178, https://openalex.org/W2789172526, https://openalex.org/W3123284220, https://openalex.org/W3011737563, https://openalex.org/W6789231957, https://openalex.org/W4391582512, https://openalex.org/W4223535187, https://openalex.org/W2919358988, https://openalex.org/W3209340886, https://openalex.org/W2052825782, https://openalex.org/W2911964244, https://openalex.org/W1965801346, https://openalex.org/W2026706435, https://openalex.org/W2008056655, https://openalex.org/W6755459668, https://openalex.org/W3201992983, https://openalex.org/W6631216910, https://openalex.org/W3005916310, https://openalex.org/W4389304912, https://openalex.org/W4400314580, https://openalex.org/W4403600933, https://openalex.org/W4382318469, https://openalex.org/W6678914141, https://openalex.org/W6679390333, https://openalex.org/W6761443735, https://openalex.org/W2005895632, https://openalex.org/W6682068783, https://openalex.org/W2236833089, https://openalex.org/W2110992644, https://openalex.org/W2084139018, https://openalex.org/W2000703258, https://openalex.org/W1986546598, https://openalex.org/W6764798969, https://openalex.org/W6665428406, https://openalex.org/W6607644983, https://openalex.org/W2618347363, https://openalex.org/W2148291626, https://openalex.org/W2939176882, https://openalex.org/W2059994457, https://openalex.org/W2954512644, https://openalex.org/W2187089797, https://openalex.org/W190583147, https://openalex.org/W2127218421 |
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| corresponding_author_ids | https://openalex.org/A5100414776, https://openalex.org/A5109012722 |
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| corresponding_institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210112150, https://openalex.org/I4210165038 |
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