OPERA：面向通用生物醫學影像分析的離線策略引導專家路由與適應

摘要

生物医学图像分析涵盖多种模态与任务，然而在实际部署中，扫描仪、协议及患者群体的严重分布偏移构成了主要障碍。高性能模型因此需要针对特定领域反复进行微调，这一高成本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），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这一部署瓶颈：从少量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，随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，实现专业化知识分配；各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；OPERA还引入分布感知自适应，利用无标签测试数据统计信息在批次级别动态调整类别权重；实例级路由通过模型间一致性与预测熵将每个样本分配给最合适的专家。我们在涵盖眼底照片、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、分割及多模态场景下的30余种基线方法进行对比。OPERA在性能与校准质量上持续提升，表明离线策略引导的专家智能体协调是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}.