生存分析领域终于有了一个不依赖离散化或分布假设的深度学习方案，做医学影像或高维数据风险建模的团队可以直接用 QSurv 获得更准确的风险曲线，值得关注。
QSurv 是一个可扩展的深度学习框架，用于非参数连续时间生存建模，避免了传统方法对时间离散化或分布假设的依赖。它采用 Gauss-Legendre 数值积分来近似累积风险函数，实现高效端到端训练。此外，QSurv 引入了时间条件低秩适配机制，使神经网络能够动态捕捉非平稳风险动态。理论分析证明了累积风险评估的误差界。在合成数据、大规模表格数据和医学影像任务上，QSurv 在预测性能和风险函数估计方面均优于现有方法。
A Scalable Nonparametric Continuous-Time Survival Model through Numerical Quadrature
Flexible continuous-time survival modeling is critical for capturing complex time-varying hazard dynamics in high-dimensional data; however, training such models remains challenging due to the intractable integral required for likelihood estimation. We introduce QSurv, a scalable deep learning framework that enables nonparametric continuous-time modeling without relying on time discretization or restrictive distributional assumptions. We propose a training objective based on Gauss-Legendre numerical quadrature, which approximates the cumulative hazard with high-order accuracy while facilitating efficient end-to-end training via standard backpropagation. Furthermore, to effectively capture non-stationary hazard dynamics in complex architectures, we introduce time-conditioned low-rank adaptation, a mechanism that conditions general neural backbones on time by dynamically modulating weights via low-rank updates. We provide theoretical analysis establishing approximation error bounds for cumulative-hazard evaluation. Comprehensive experiments across synthetic benchmarks, large-scale real-world tabular datasets, and high-dimensional medical imaging tasks demonstrate that QSurv achieves competitive predictive performance with advantages in instantaneous hazard function estimation, enabling more interpretable characterization of time-varying risk patterns.