  Survival analysis models time-to-event distributions with censorship.
Recently, deep survival models using neural networks have dominated due to
their representational power and state-of-the-art performance. However, their
"black-box" nature hinders interpretability, which is crucial in real-world
applications. In contrast, "white-box" tree-based survival models offer better
interpretability but struggle to converge to global optima due to greedy
expansion. In this paper, we bridge the gap between previous deep survival
models and traditional tree-based survival models through deep rectified linear
unit (ReLU) networks. We show that a deliberately constructed deep ReLU network
(SurvReLU) can harness the interpretability of tree-based structures with the
representational power of deep survival models. Empirical studies on both
simulated and real survival benchmark datasets show the effectiveness of the
proposed SurvReLU in terms of performance and interoperability. The code is
available at \href{https://github.com/xs018/SurvReLU}{\color{magenta}{
https://github.com/xs018/SurvReLU}}.
