Ocular Disease Recognition via Differential Privacy and Unsupervised Domain Regularizer

Adopting deep learning in early fundus screening images benefits ocular disease recognition and helps patients avoid blindness in recent years. The robust data representation capability of deep learning is derived from numerous data and annotations. However, the fundus images collected from hospitals or institutes have privacy issues and obvious domain gaps, which greatly influence multi-site learning performance. In this work, a learning system with differential privacy and unsupervised domain regularizer is proposed for ocular disease recognition. First, a Laplace randomized mechanism is introduced to keep the privacy of local models and a global model is constructed via a weighted sum process. Second, an unsupervised domain regularizer, which converts the last fully-connected layer into two sub-layers and then adopts an MMD loss in the element-wise layers of source and target domains, is proposed for unsupervised domain adaptation. Numerous experiments, including four different settings, verify the performance in a multi-disease ocular dataset.