Discriminative atoms embedding relation dual network for classification of choroidal neovascularization in OCT images

Choroidal neovascularization (CNV) is an eye disease that can cause vision loss. Automatic CNV classification in OCT images is crucial in the treatment of CNV. However, two problems arise for CNV classification in OCT images. The subtle visual differences between different CNV types render classification difficult. Additionally, it is difficult to obtain sufficient labeled data, which results in performance degradation. In order to solve these two problems, a discriminative atom-embedding relation dual network is proposed in this paper. Considering that semi-supervised learning (SSL) is an effective machine learning framework to make full use of limited labeled data and a large amount of unlabeled data, the proposed network is developed within an SSL framework. To capture the visual differences, novel discriminative atoms are first introduced to mine discriminative information between different CNV types. Subsequently, a relation module is incorporated to embed the learned discriminative atom information into the features. This makes the learned features capable of distinguishing between different CNV types. Moreover, a novel relation consistency loss is proposed to further improve the robustness of the learned features. Experimental results on private and public datasets demonstrate the effectiveness of the proposed method. •A novel discriminative atoms embedding relation dual network is proposed to address the challenge of inter-class similarities in CNV classification with limited labeled data.•To represent the discriminative details of CNV latently, discriminative atoms are introduced with incorporation of dynamic discriminative learning mechanism and constraint of discrimination loss.•In order to learn the discriminative features necessary to distinguish different CNV class, a novel relation learning module is introduced to embed the discriminative atoms into the learned features.•A dual mean-teacher network architecture is developed, and a relation consistency loss is proposed to guarantee that discriminative features can be learned during the utilization of unlabeled data.