Survival analysis on tabular data is a well-studied problem. However, existing deep learning methods are often highly task-specific, which can limit the transfer of new approaches from other domains and introduce constraints that may affect performance. We propose TabSurv, an approach that adapts modern tabular architectures to survival analysis using either the Weibull distribution or non-parametric survival prediction. TabSurv optimizes SurvHL, a novel histogram loss function supporting censored data. In addition to a baseline feed-forward network, we implement deep ensembles of MLPs for survival analysis within TabSurv. In contrast to prior work, the ensemble components are trained in parallel, optimizing survival distribution parameters before averaging, which promotes diversity across ensemble component predictions. We perform a comprehensive empirical evaluation of different proposed architectures on 10 diverse real-world survival datasets. Our results show that TabSurv consistently outperforms on average established classical and deep learning baselines, such as RSF, DeepSurv, DeepHit, SurvTRACE. Notably, deep ensembles with Weibull parametrization instead of non-parametric models achieve the highest average rank by C-index. Overall, our study clarifies how modern tabular neural networks can be adapted and trained to tackle survival analysis problems, offering a strong and reliable approach. The TabSurv implementation is publicly available.

翻译：表格数据的生存分析是一个研究充分的问题。然而，现有深度学习方法往往高度任务特定，这可能限制其他领域新方法的迁移，并引入可能影响性能的约束。我们提出TabSurv，一种通过使用威布尔分布或非参数生存预测，将现代表格架构适配至生存分析的方法。TabSurv优化了SurvHL，一种支持删失数据的新型直方图损失函数。除基线前馈网络外，我们还在TabSurv中实施了用于生存分析的MLP深度集成。与先前工作不同，集成组件并行训练，在平均前优化生存分布参数，这促进了集成组件预测间的多样性。我们在10个多样化的真实生存数据集上对所提出的不同架构进行了全面的实证评估。结果表明，TabSurv在平均性能上始终优于已建立的经典和深度学习基线，如RSF、DeepSurv、DeepHit、SurvTRACE。值得注意的是，采用威布尔参数化的深度集成而非非参数模型在C-index上获得了最高平均排名。总体而言，我们的研究阐明了如何适配和训练现代表格神经网络以解决生存分析问题，提供了一种强大且可靠的方法。TabSurv的实现已公开可用。