The Cox proportional hazard model and random survival forests (RSF) are useful semi-parametric and non-parametric methods in modeling time-to-event data. However, both approaches may fail in case of small sample size and/or high censoring rate. In this research, we want to tackle such problems within the random forests framework using semi-supervised data transduction techniques and a layer-by-layer processing similar to deep forest. Experiments from both extensive simulated data and real-world benchmark datasets have shown that the proposed deep survival forests (DSF) outperforms Cox, RSF by a noticeable margin and also work better than several state-of-art survival ensembles including Cox boosting models and latest survival forest extensions on a variety of scenarios. The superiority of DSF stands out when small sample-sized and highly censored data are confronted.