OPERA：离线策略引导的专家路由与自适应通用生物医学图像分析

摘要

生物医学图像分析涵盖多种成像模态和任务，但实际部署受限于扫描仪、协议和患者群体之间的严重分布偏移。高性能模型因此需要反复进行领域特定微调，这一代价高昂的循环在标签稀缺或隐私限制数据共享时变得不可行。我们提出OPERA（离线策略引导的专家路由与自适应），这是一种多智能体集成框架，通过将专家权重分配视为离线策略学习问题来解决部署瓶颈：从一个小型验证集中学习路由策略，无需对任何专家智能体进行梯度更新，然后结合测试时自适应以应对分布偏移。OPERA通过互补机制协调异构专业智能体：专家画像模块离线学习选择策略，实现专业知识的合理分配；每个智能体通过温度调整进行置信度校准，确保更可靠的概率输出；还引入分布感知自适应，利用未标记测试数据的统计量在批级别动态调整类别权重。在实例级别，通过模型间一致性和预测熵将每个样本分配给最合适的专家。我们在涵盖眼底摄影、胸部X光、CT、MRI及多模态诊断基准的9个数据集上评估OPERA，与分类、分割和多模态场景下的30余种基线方法进行比较。OPERA持续提升了性能与校准质量，表明离线策略引导的专家智能体协调是一种无需重新训练即可实现可部署生物医学AI的实用路径。代码见https://github.com/HUANGLIZI/OPERA{GitHub}。

English

Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distribution shifts across scanners, protocols, and patient populations. High-performing models consequently require repeated domain-specific fine-tuning, which is a costly cycle that becomes impractical when labels are scarce or privacy constraints limit data sharing. We propose OPERA (Offline Policy-guided Expert Routing and Adaptation), a multi-agent ensemble framework that addresses this deployment bottleneck by treating expert weight assignment as an offline policy learning problem: a routing policy is learned from a small validation set without gradient updates to any expert agent, then deployed with test-time adaptation to handle distribution shift. OPERA coordinates heterogeneous specialist agents through complementary mechanisms. The expert profiling module learns selection policies offline, enabling informed allocation of expertise. Each agent undergoes confidence calibration through temperature adjustment, ensuring more reliable probabilistic outputs. OPERA also incorporates distribution aware adaptation, where class weights are dynamically adjusted at the batch level using statistics derived from unlabeled test data. Instance level routing assigns each sample to the most suitable expert by leveraging inter model agreement and predictive entropy. We evaluate OPERA on 9 datasets covering fundus photography, chest X-ray, CT, MRI, and multimodal diagnostic benchmarks, comparing against 30+ baselines across classification, segmentation, and multimodal settings. OPERA consistently improves performance and calibration quality, demonstrating that offline policy-guided expert agents coordination is a practical path to deployable biomedical AI without retraining. Code is on https://github.com/HUANGLIZI/OPERA{GitHub}.