  The influx of deep learning (DL) techniques into the field of survival
analysis in recent years has led to substantial methodological progress; for
instance, learning from unstructured or high-dimensional data such as images,
text or omics data. In this work, we conduct a comprehensive systematic review
of DL-based methods for time-to-event analysis, characterizing them according
to both survival- and DL-related attributes. In summary, the reviewed methods
often address only a small subset of tasks relevant to time-to-event data -
e.g., single-risk right-censored data - and neglect to incorporate more complex
settings. Our findings are summarized in an editable, open-source, interactive
table: https://survival-org.github.io/DL4Survival. As this research area is
advancing rapidly, we encourage community contribution in order to keep this
database up to date.
