  Deep learning (DL) has made significant progress in angle closure
classification with anterior segment optical coherence tomography (AS-OCT)
images. These AS-OCT images are often acquired by different imaging
devices/conditions, which results in a vast change of underlying data
distributions (called "data domains"). Moreover, due to practical labeling
difficulties, some domains (e.g., devices) may not have any data labels. As a
result, deep models trained on one specific domain (e.g., a specific device)
are difficult to adapt to and thus may perform poorly on other domains (e.g.,
other devices). To address this issue, we present a multi-target domain
adaptation paradigm to transfer a model trained on one labeled source domain to
multiple unlabeled target domains. Specifically, we propose a novel Multi-scale
Multi-target Domain Adversarial Network (M2DAN) for angle closure
classification. M2DAN conducts multi-domain adversarial learning for extracting
domain-invariant features and develops a multi-scale module for capturing local
and global information of AS-OCT images. Based on these domain-invariant
features at different scales, the deep model trained on the source domain is
able to classify angle closure on multiple target domains even without any
annotations in these domains. Extensive experiments on a real-world AS-OCT
dataset demonstrate the effectiveness of the proposed method.
