  Survival analysis is a widely known method for predicting the likelihood of
an event over time. The challenge of dealing with censored samples still
remains. Traditional methods, such as the Cox Proportional Hazards (CPH) model,
hinge on the limitations due to the strong assumptions of proportional hazards
and the predetermined relationships between covariates. The rise of models
based on deep neural networks (DNNs) has demonstrated enhanced effectiveness in
survival analysis. This research introduces the Implicit Continuous-Time
Survival Function (ICTSurF), built on a continuous-time survival model, and
constructs survival distribution through implicit representation. As a result,
our method is capable of accepting inputs in continuous-time space and
producing survival probabilities in continuous-time space, independent of
neural network architecture. Comparative assessments with existing methods
underscore the high competitiveness of our proposed approach. Our
implementation of ICTSurF is available at https://github.com/44REAM/ICTSurF.
