Deep Contrastive Survival Analysis with Dual-View Clustering

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.