MedPEFT-CL：基于医学语义适配器和双向记忆巩固的双阶段参数高效持续学习
MedPEFT-CL: Dual-Phase Parameter-Efficient Continual Learning with Medical Semantic Adapter and Bidirectional Memory Consolidation
AI总结：
MedPEFT-CL通过语义驱动适配器和双向记忆协调，实现医学视觉-语言任务中的参数高效持续学习，有效缓解灾难性遗忘。
摘要：
医学视觉-语言分割模型在适应新解剖结构时会出现灾难性遗忘，需要完全重新训练，限制了其临床应用。尽管持续学习方法已被研究用于各种应用，但针对医学视觉-语言任务的持续学习方法仍有待深入研究。我们提出了MedPEFT-CL，一种参数高效的持续学习框架，通过基于CLIPSeg的双阶段架构，同时解决新任务的高效学习和先前知识的保存。我们的双阶段架构包含一个自适应学习阶段，通过语义相似性驱动的适配器分配和基于提示相似性分析的参数高效微调，用于医学任务；以及一个知识巩固阶段，采用双向Fisher-记忆协调。这形成了一个增强的循环：巩固指导重播优先级，而新任务提供具有挑战性的样本，从而提高保留策略。我们的主要贡献是：（1）语义驱动的适配器分配机制，能够高效学习新的医学任务；（2）双模态LoRA适应，显著减少可训练参数同时保持跨模态学习；（3）双向Fisher-记忆协调，防止先前医学任务的灾难性遗忘。在多样化的医学数据集上进行的广泛实验显示，该框架在最小参数开销下实现了优越的遗忘缓解和性能保留，使其在医学视觉-语言场景中的持续学习应用有效。
英文摘要：
Medical vision-language segmentation models suffer from catastrophic forgetting when adapting to new anatomical structures, requiring complete retraining that limits their clinical deployment. Although continual learning approaches have been studied for various applications, targeted research on continual learning approaches specifically designed for medical vision-language tasks remains underexplored. We propose MedPEFT-CL, a parameter-efficient continual learning framework that addresses both efficient learning of new tasks and preservation of previous knowledge through a dual-phase architecture based on CLIPSeg. Our dual-phase architecture features an adaptive learning phase that employs semantic similarity-based adapter allocation and parameter-efficient fine-tuning for medical tasks through prompt similarity analysis, and a knowledge consolidation phase employing bi-directional Fisher-memory coordination. This creates a reinforcing cycle: consolidation directs replay priorities while new tasks provide challenging samples that improve retention strategies. Our key contributions are: (1) a semantic-driven adapter allocation mechanism that enables efficient learning of new medical tasks, (2) a bi-modal LoRA adaptation that significantly reduces trainable parameters while maintaining cross-modal learning, and (3) bidirectional Fisher-memory coordination that prevents catastrophic forgetting from previous medical tasks. Extensive experiments across diverse medical datasets demonstrate superior forgetting mitigation and performance retention with minimal parameter overhead, making the framework effective for continual learning in medical vision-language scenarios.
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