用对比学习提升生存分析判别力，不牺牲校准性。
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning
- 基于生存结局感知的加权对比学习，相似结局样本惩罚更小。
- 在多个临床数据集上，判别力与校准性均优于现有模型。
- 适合需要高可靠性预测的医疗生存分析场景。
以往深度生存分析方法主要依赖排序损失提升判别性能，但常以牺牲校准性能为代价。为此，我们提出一种新型对比学习方法，旨在提升判别力而不损害校准性。该方法在对比学习框架中引入加权采样，对生存结局相近的样本赋予较低惩罚，契合‘事件时间相似则临床状态相似’的假设。结合常用的负对数似然损失，该方法在不直接调整模型输出的前提下，显著提升判别性能，同时实现更好校准。在多个真实世界临床数据集上的实验表明，本方法在判别力与校准性两方面均超越当前最优深度生存模型。通过全面的消融实验，定量与定性分析进一步验证了方法的有效性。
原文摘要 · Abstract (English)
Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrastive learning approach specifically designed to enhance discrimination \textit{without} sacrificing calibration. Our method employs weighted sampling within a contrastive learning framework, assigning lower penalties to samples with similar survival outcomes. This aligns well with the assumption that patients with similar event times share similar clinical statuses. Consequently, when augmented with the commonly used negative log-likelihood loss, our approach significantly improves discrimination performance without directly manipulating the model outputs, thereby achieving better calibration. Experiments on multiple real-world clinical datasets demonstrate that our method outperforms state-of-the-art deep survival models in both discrimination and calibration. Through comprehensive ablation studies, we further validate the effectiveness of our approach through quantitative and qualitative analyses.
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