足迹引导的无示例持续病理报告生成
Footprint-Guided Exemplar-Free Continual Histopathology Report Generation
AI总结：
本文提出一种无示例的持续学习框架，通过构建紧凑的领域足迹实现WSI到报告的生成，支持生成性重放并适应变化的临床报告规范。
摘要：
视觉-语言模型的快速进展使从吉像素整片图像生成病理报告成为可能，但大多数方法假设静态训练并同时访问所有数据。然而，在临床部署中，随着时间的推移，新的器官、机构和报告规范会出现，连续微调会导致灾难性遗忘。我们引入了一种无示例的持续学习框架，用于WSI到报告生成，该框架不存储原始切片或片段示例。核心思想是在冻结的片段嵌入空间中构建紧凑的领域足迹：一个代表形态令牌的小代码本，加上滑片级别的共现摘要和轻量级片段计数先验。这些足迹通过合成伪WSI表示来支持生成性重放，这些表示反映了领域特定的形态混合，同时教师快照提供伪报告以监督更新模型，而无需保留过去数据。为了解决变化的报告规范，我们将领域特定的语言特征提炼成紧凑的风格描述符，并用它来引导生成。在推理时，模型直接从切片信号中识别最兼容的描述符，从而在不需要显式领域标识符的情况下实现领域无关的设置。在多个公共持续学习基准上评估，我们的方法优于无示例和有限缓冲回放基线，突显了基于足迹的生成性重放作为在演变临床环境中的实用解决方案。
英文摘要：
Rapid progress in vision-language modeling has enabled pathology report generation from gigapixel whole-slide images, but most approaches assume static training with simultaneous access to all data. In clinical deployment, however, new organs, institutions, and reporting conventions emerge over time, and sequential fine-tuning can cause catastrophic forgetting. We introduce an exemplar-free continual learning framework for WSI-to-report generation that avoids storing raw slides or patch exemplars. The core idea is a compact domain footprint built in a frozen patch-embedding space: a small codebook of representative morphology tokens together with slide-level co-occurrence summaries and lightweight patch-count priors. These footprints support generative replay by synthesizing pseudo-WSI representations that reflect domain-specific morphological mixtures, while a teacher snapshot provides pseudo-reports to supervise the updated model without retaining past data. To address shifting reporting conventions, we distill domain-specific linguistic characteristics into a compact style descriptor and use it to steer generation. At inference, the model identifies the most compatible descriptor directly from the slide signal, enabling domain-agnostic setup without requiring explicit domain identifiers. Evaluated across multiple public continual learning benchmarks, our approach outperforms exemplar-free and limited-buffer rehearsal baselines, highlighting footprint-based generative replay as a practical solution for deployment in evolving clinical settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
