MergeSurv：用于全切片图像生存分析的基于合并的持续学习
MergeSurv: Merging-Based Continual Learning for Survival Analysis on Whole-Slide Images
AI总结：
研究针对全切片图像生存分析，提出MergeSurv框架，独立微调病理视觉语言基础模型参数后合并，还探究两种推理策略，实验表明其优于其他方法且能减少灾难性遗忘。
摘要：
全切片图像（WSIs）的生存分析在计算病理学中对预后估计和治疗规划很重要。现有生存模型通常为每个癌症队列单独训练，对千兆像素级WSIs进行持续适应计算成本高昂。本研究提出MergeSurv，一种用于WSI生存分析的基于合并的持续学习框架。在每个任务上独立微调病理视觉语言基础模型，学习到的参数顺序合并到统一模型中，无需存储先前训练数据。我们进一步研究了两种推理策略：全对一（OFA）和投票专家聚合（VEA）。在四个TCGA队列上的实验表明，MergeSurv优于朴素微调以及基于正则化和基于排练的代表性持续学习方法，同时有效减少灾难性遗忘量。结果表明，模型合并是计算病理学中可扩展和隐私保护持续学习的一个有前景的方向。
英文摘要：
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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