让AI在医疗数据中自动过滤无效样本，提升标注效率。
PromptGate Client Adaptive Vision Language Gating for Open Set Federated Active Learning
- 用可学习的提示向量动态优化视觉语言模型，适应本地医疗场景。
- 保持95%以上真实数据纯度，识别98%异常样本，显著优于传统方法。
- 无需共享数据，适配各类主动学习策略，适合资源有限的医疗机构。
在资源受限的医疗机构部署医疗AI需要高效的数据学习流程并保护患者隐私。联邦学习（FL）可在不集中数据的前提下协作训练医疗AI，但真实临床数据池本质上是开放集，包含成像伪影、模态错误等分布外（OOD）噪声。传统主动学习（AL）查询策略常将这些噪声误判为有效样本，浪费稀缺的标注预算。本文提出PromptGate，一种面向开放集联邦主动学习的动态视觉语言模型门控框架，在查询前净化未标注数据池。PromptGate引入联邦类别特异性上下文优化：轻量级可学习提示向量，适配冻结的BiomedCLIP骨干网络，通过FedAvg全局聚合，不共享患者数据。随着新标注数据到来，提示逐步强化类内/类外边界，使VLM成为动态守门人，策略无关——可作为即插即用的预筛选模块，提升任意下游主动学习策略性能。在分布式皮肤科和乳腺影像基准上的实验表明，静态VLM提示会将真实数据纯度降至50%，而PromptGate维持超过95%纯度，并实现98%的OOD召回率。
原文摘要 · Abstract (English)
Deploying medical AI across resource-constrained institutions demands data-efficient learning pipelines that respect patient privacy. Federated Learning (FL) enables collaborative medical AI without centralising data, yet real-world clinical pools are inherently open-set, containing out-of-distribution (OOD) noise such as imaging artifacts and wrong modalities. Standard Active Learning (AL) query strategies mistake this noise for informative samples, wasting scarce annotation budgets. We propose PromptGate, a dynamic VLM-gated framework for Open-Set Federated AL that purifies unlabeled pools before querying. PromptGate introduces a federated Class-Specific Context Optimization: lightweight, learnable prompt vectors that adapt a frozen BiomedCLIP backbone to local clinical domains and aggregate globally via FedAvg -- without sharing patient data. As new annotations arrive, prompts progressively sharpen the ID/OOD boundary, turning the VLM into a dynamic gatekeeper that is strategy-agnostic: a plug-and-play pre-selection module enhancing any downstream AL strategy. Experiments on distributed dermatology and breast imaging benchmarks show that while static VLM prompting degrades to 50% ID purity, PromptGate maintains $>$95% purity with 98% OOD recall.
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