Deployment of Deep Neural Networks in medical imaging is hindered by distribution shift between training data and data processed after deployment, causing performance degradation. Post-Deployment Adaptation (PDA) addresses this by tailoring a pre-trained, deployed model to the target data distribution using limited labelled or entirely unlabelled target data, while assuming no access to source training data as they cannot be deployed with the model due to privacy concerns and their large size. This makes reliable adaptation challenging due to limited learning signal. This paper challenges this assumption and introduces FedPDA, a novel adaptation framework that brings the utility of learning from remote data from Federated Learning into PDA. FedPDA enables a deployed model to obtain information from source data via remote gradient exchange, while aiming to optimize the model specifically for the target domain. Tailored for FedPDA, we introduce a novel optimization method StarAlign (Source-Target Remote Gradient Alignment) that aligns gradients between source-target domain pairs by maximizing their inner product, to facilitate learning a target-specific model. We demonstrate the method's effectiveness using multi-center databases for the tasks of cancer metastases detection and skin lesion classification, where our method compares favourably to previous work. Code is available at: https://github.com/FelixWag/StarAlign

翻译：深度神经网络在医学影像中的部署受到训练数据与部署后处理数据之间分布偏移的阻碍，导致性能下降。部署后自适应（PDA）通过利用有限标注或完全无标注的目标数据，对预训练部署模型进行定制化调整以适配目标数据分布，同时假设无法访问源训练数据——这些数据因隐私问题及体量庞大而无法随模型一同部署。这种有限的学习信号使得可靠的自适应极具挑战性。本文质疑这一假设，提出FedPDA——一种将联邦学习中远程数据学习效用引入PDA的新型自适应框架。FedPDA使部署模型能够通过远程梯度交换从源数据中获取信息，同时针对目标域优化模型。针对FedPDA，我们提出了一种新型优化方法StarAlign（源-目标远程梯度对齐），该方法通过最大化源-目标域对之间的内积来对齐梯度，以促进学习目标特异性模型。我们使用多中心数据库在癌症转移检测和皮肤病变分类任务中验证了该方法的有效性，其结果优于先前工作。代码地址：https://github.com/FelixWag/StarAlign