锻造动态记忆：面向通用医学基础模型的检索引导持续学习
Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Models
将检索增强生成（RAG）与持续学习结合，通过动态知识蒸馏缓解多模态医学VLM在领域迁移中的灾难性遗忘，为视觉-语言模型的持续学习提供可复用框架。
原文依据 we pioneer the integration of Retrieval-Augmented Generation (RAG) into CL
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论多模态生物医学视觉-语言模型（VLM）在持续学习（CL）领域展现出巨大潜力。然而，它们面临一个核心困境：如何在保留细粒度模态内特征的同时，弥合不同模态间的显著领域差距。为解决这一挑战，我们提出了一个综合框架。利用我们从PubMed科学论文中构建的包含1800万条数据的多模态综合医学检索数据库，我们率先将检索增强生成（RAG）集成到持续学习中。具体而言，我们采用多模态、多层次的RAG系统，通过动态、按需的知识检索为模型微调提供实时指导。在此基础上，我们引入了动态知识蒸馏框架。该框架通过根据所需细节程度动态调节参数空间的重要性、蒸馏知识的粒度以及参考数据集的数据分布，精确解决了上述核心困境。为全面验证我们策略的临床价值，我们设计了一个更严格的医学通才任务增量学习（MGTIL）基准。该基准旨在同时评估模型对显著领域偏移的适应能力、对细微域内特征的保留能力以及对新颖复杂医学任务的实时学习能力。我们的方法在所有指标上均取得稳定的SOTA结果，代码见https://github.com/CZZZZZZZZZZZZZZZZZ/PRIMED。
英文原文摘要 1566 字符
Multimodal biomedical Vision-Language Models (VLMs) exhibit immense potential in the field of Continual Learning (CL). However, they confront a core dilemma: how to preserve fine-grained intra-modality features while bridging the significant domain gap across different modalities. To address this challenge, we propose a comprehensive framework. Leveraging our 18-million multimodal and comprehensive medical retrieval database derived from PubMed scientific papers, we pioneer the integration of Retrieval-Augmented Generation (RAG) into CL. Specifically, we employ a multi-modal, multi-layer RAG system that provides real-time guidance for model fine-tuning through dynamic, on-demand knowledge retrieval. Building upon this, we introduce a dynamic knowledge distillation framework. This framework precisely resolves the aforementioned core dilemma by dynamically modulating the importance of the parameter space, the granularity of the distilled knowledge, and the data distribution of the reference dataset in accordance with the required level of detail. To thoroughly validate the clinical value of our strategy, we have designed a more rigorous Medical Generalist Task Incremental Learning (MGTIL) benchmark. This benchmark is engineered to simultaneously evaluate the model's capacity for adaptation to significant domain shifts, retention of subtle intra-domain features, and real-time learning of novel and complex medical tasks. Our method achieves stable SOTA results across all metrics, with https://github.com/CZZZZZZZZZZZZZZZZZ/PRIMED code available.