通过模型合并实现病理图像生存分析的持续学习，避免遗忘且无需存储原始数据。
MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images
- 用参数合并替代重训练，逐步集成新癌症队列知识。
- 在四个TCGA队列上超越传统方法，遗忘率显著降低。
- 适合需隐私保护和高效更新的临床病理场景。
全切片图像（WSI）上的生存分析在计算病理学中对预后评估和治疗规划至关重要。然而，现有生存模型通常针对每个癌症队列独立训练，导致在超大尺度WSI上持续适应时计算成本高昂。本文提出一种基于合并的持续学习框架MergeSurv，用于WSI生存分析。每个任务均独立微调一个病理视觉-语言基础模型，随后将学习到的参数序列式合并至统一模型中，无需存储先前训练数据。我们进一步研究两种推理策略：一次性全部预测（OFA）与投票专家聚合（VEA）。在四个TCGA队列上的实验表明，MergeSurv优于简单的微调方法以及代表性的正则化与回放类持续学习方法，同时有效缓解灾难性遗忘。结果表明，模型合并是计算病理学中可扩展、隐私友好的持续学习的有前景方向。
原文摘要 · Abstract (English)
Survival analysis on Whole Slide Images (WSIs) is important in computational pathology for prognosis estimation and treatment planning. However, existing survival models are typically trained independently for each cancer cohort, making continual adaptation computationally expensive for gigapixel-scale WSIs. In this study, we propose MergeSurv, a merging-based continual learning framework for WSI survival analysis. A pathology vision-language foundation model is independently fine-tuned on each task, and the learned parameters are sequentially merged into a unified model without storing previous training data. We further investigate two inference strategies: One-for-All (OFA) and Voting-Expert Aggregation (VEA). Experiments on four TCGA cohorts demonstrate that MergeSurv outperforms naive fine-tuning as well as representative regularization-based and rehearsal-based continual learning methods, while effectively reducing catastrophic forgetting. The results suggest that model merging is a promising direction for scalable and privacy-preserving continual learning in computational pathology.
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