The influx of deep learning (DL) techniques into the field of survival analysis in recent years, coupled with the increasing availability of high-dimensional omics data and unstructured data like images or text, has led to substantial methodological progress; for instance, learning from such high-dimensional or unstructured data. Numerous modern DL-based survival methods have been developed since the mid-2010s; however, they often address only a small subset of scenarios in the time-to-event data setting - e.g., single-risk right-censored survival tasks - and neglect to incorporate more complex (and common) settings. Partially, this is due to a lack of exchange between experts in the respective fields. In this work, we provide a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related attributes. In doing so, we hope to provide a helpful overview to practitioners who are interested in DL techniques applicable to their specific use case as well as to enable researchers from both fields to identify directions for future investigation. We provide a detailed characterization of the methods included in this review as an open-source, interactive table: https://survival-org.github.io/DL4Survival. As this research area is advancing rapidly, we encourage the research community to contribute to keeping the information up to date.

翻译：近年来，深度学习技术涌入生存分析领域，加上高维组学数据以及图像或文本等非结构化数据日益普及，推动了方法论上的重大进展——例如，从此类高维或非结构化数据中学习。自2010年代中期以来，已开发出众多基于深度学习的现代生存分析方法。然而，这些方法往往仅适用于时间至事件数据设置中的少数场景——例如，单风险右删失的生存任务——而忽略了更复杂（且常见）的设置。部分原因在于相关领域专家之间缺乏交流。在本工作中，我们对基于深度学习的时间至事件分析方法进行了全面的系统性综述，并根据生存分析和深度学习两方面的属性对其进行分类。借此，我们希望为对适用于其特定用例的深度学习技术感兴趣的实践者提供一份有益的概览，同时也使两个领域的研究者能够识别未来研究方向。我们以开源、交互式表格的形式提供了本综述所涉及方法的详细特征描述：https://survival-org.github.io/DL4Survival。鉴于该研究领域发展迅速，我们鼓励研究社区共同维护信息更新。