Feature enhancement network for CNV typing in optical coherence tomography images

Objective. Choroidal neovascularization (CNV) is a characteristic feature of wet age-related macular degeneration, which is one of the main causes of blindness in the elderly. Automatic classification of CNV in optical coherence tomography images plays an auxiliary role in the clinical treatment of CNV. Approach. This study proposes a feature enhancement network (FE-net) to discriminate between different CNV types with high inter-class similarity. The FE-net consists of two branches: discriminative FE and diverse FE. In the discriminative FE branch, a novel class-specific feature extraction module is introduced to learn class-specific features, and the discriminative loss is introduced to make the learned features more discriminative. In the diverse FE branch, the attention region selection is used to mine the multi-attention features from feature maps in the same class, and the diverse loss is introduced to guarantee that the attention features are different, which can improve the diversity of the learned features. Main results. Experiments were conducted on our CNV dataset, with significant accuracy of 92.33%, 87.45%, 90.10%, and 91.25% on ACC, AUC, SEN, and SPE, respectively. Significance. These results demonstrate that the proposed method can effectively learn the discriminative and diverse features to discriminate subtle differences between different types of CNV. And accurate classification of CNV plays an auxiliary role in clinical treatmen.