面向提示的自适应弹性权重固化用于医学视觉-语言模型的持续学习
Prompt-Aware Adaptive Elastic Weight Consolidation for Continual Learning in Medical Vision-Language Models
AI总结：
PA-EWC通过提示引导的参数专门化，有效缓解医疗视觉-语言模型在持续学习中的灾难性遗忘问题，提升多模态医学任务的性能。
摘要：
医疗AI系统在临床环境中部署时面临灾难性遗忘问题，其中模型必须在保留先前诊断能力的同时学习新的成像协议。这一挑战对于必须在多种成像模态之间保持复杂跨模态对齐的医学视觉-语言模型尤为突出。我们介绍了Prompt-Aware Adaptive Elastic Weight Consolidation（PA-EWC），一种新的持续学习方法，通过提示引导的参数专门化来解决灾难性遗忘问题。我们的方法系统地根据模型参数在处理视觉描述、空间引导和医学语义信息中的功能角色进行分类，从而在保护关键知识的同时允许适应新的临床需求。PA-EWC结合了自适应Fisher信息计算与梯度稳定性分析，并基于医学术语密度开发了加权复杂性度量。我们评估了我们的方法在五个医学成像数据集（Kvasir-SEG、ISIC 2018、CheXlocalize、BUSI、CAMUS）上的表现，这些数据集代表了包括内窥镜、皮肤镜、放射学和超声在内的多种模态。实验结果表明，与基线方法相比，PA-EWC将灾难性遗忘减少了高达17.58%，在胸部X光病理定位任务上性能提高了4.30%，在息肉分割任务上提高了6.06%。
英文摘要：
Medical AI systems face catastrophic forgetting when deployed in clinical settings, where models must learn new imaging protocols while retaining prior diagnostic capabilities. This challenge is particularly acute for medical vision-language models that must preserve complex cross-modal alignments between medical images and clinical terminology across diverse imaging modalities. We introduce Prompt- Aware Adaptive Elastic Weight Consolidation (PA-EWC), a novel continual learning approach that addresses catastrophic forgetting through prompt-guided parameter specialization. Our method systematically categorizes model parameters based on their functional roles in processing visual-descriptive, spatial-guided, and medical-semantic information, enabling targeted protection of critical knowledge while allowing adaptation to new clinical requirements. PA-EWC incorporates adaptive Fisher Information computation with gradient stability analysis and develops weighted complexity metrics based on medical terminology density. We evaluate our approach across five medical imaging datasets (Kvasir-SEG, ISIC 2018, CheXlocalize, BUSI, CAMUS) representing diverse modalities including endoscopy, dermoscopy, radiography, and ultrasound. Experimental results demonstrate that PA-EWC reduces catastrophic forgetting by up to 17.58% compared to baseline methods, with performance improvements of 4.30% on chest X-ray pathology localization and 6.06% on polyp segmentation.
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