AI 中文总结
针对任务异构的多标签医学图像分类问题，提出解析联邦学习框架，通过平衡标签投影、每类绝对聚合法则等闭式操作，最多两轮通信，优于FedMLP，提升分类指标并减少通信量。
AI 中文摘要
联邦学习（FL）使多个临床机构能够协作训练共享疾病分类器，而无需集中患者数据。然而，在实际中，每个机构仅标注其专业领域内的病理情况，因此联邦在任务异构情况下运行：每个客户端仅持有目标疾病类别的严格子集标签，而其他类别在该站点完全未被观察到。现有基于梯度的FL方法在此设置下失败，因为它们需要数百轮通信才能收敛，并且缺失类标签会引入系统的假阴性偏差，若无原则机制模型无法纠正。我们提出了一种用于任务异构下多标签医学图像分类的解析联邦学习框架。该方法用三个闭式操作取代迭代梯度优化：平衡标签投影，通过归一化正负贡献以平衡类不平衡偏差；每类绝对聚合法则，从标注客户端上传的充分统计量中独立组装每个疾病类别的最优岭回归分类器；可选的解析伪标签细化轮，将缺失类知识从置信过滤的教师分类器传播到未标注客户端。整个过程最多需要两轮通信。在ChestXray14上的实验表明，该方法始终优于最先进的联邦多标签方法FedMLP，最高可提高18.44 BACC点和13.24 AUC点，同时减少通信量。
英文摘要
Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data. In practice, however, each institution annotates only the pathologies within its area of expertise, so the federation operates under task heterogeneity: each client holds labels for a strict subset of the target disease categories while the remaining classes are entirely unobserved at that site. Existing gradient-based FL methods fail under this setting because they require hundreds of communication rounds to converge and because missing class labels introduce systematic false-negative bias that the model cannot correct without a principled mechanism. We propose an analytic federated learning framework for multi-label medical image classification under task heterogeneity. The proposed method replaces iterative gradient optimization with three closed-form operations: a balanced label projection that neutralizes class-imbalance bias by normalizing positive and negative contributions to equal total mass; a per-class absolute aggregation law that independently assembles the optimal ridge-regression classifier for each disease category from the sufficient statistics uploaded by its annotating clients; and an optional analytic pseudo-label refinement round that propagates missing-class knowledge from a confidence-filtered teacher classifier to non-annotating clients. The entire procedure requires at most two communication rounds, irrespective of the degree of task heterogeneity or the number of participating clients. Experiments on ChestXray14 under four progressively severe missing-class configurations demonstrate that the proposed method consistently outperforms the state-of-the-art federated multi-label method FedMLP by up to 18.44 BACC points and 13.24 AUC points, while reducing the communication.
CommentsThe 14th IEEE International Conference on Healthcare Informatics
