Distinguishing Differences Matters: Focal Contrastive Network for Peripheral Anterior Synechiae Recognition

Yifan Yang, Huihui Fang, Qing Du, Fei Li, Xiulan Zhang, Mingkui Tan, Yanwu Xu

Abstract: We address the problem of Peripheral Anterior Synechiae (PAS) recognition, which aids clinicians in better understanding the progression of the type of irreversible angle-closure glaucoma. Clinical identification of PAS requires indentation gonioscopy, which is patient-contacting and time-consuming. Thus, we aim to design an automatic deep-learning-based method for PAS recognition based on non-contacting anterior segment optical coherence tomography (AS-OCT). However, modeling structural differences between tissues, which is the key for clinical PAS recognition, is especially challenging for deep learning methods. Moreover, the class imbalance issue and the tiny region of interest (ROI) hinder the learning process. To address these issues, we propose a novel Focal Contrastive Network (FC-Net), which contains a Focal Contrastive Module (FCM) and a Focal Contrastive (FC) loss to model the structural differences of tissues, and facilitate the learning of hard samples and minor class. Meanwhile, to weaken the impact of irrelevant structure, we introduce a zoom-in head to localize the tiny ROI. Extensive experiments on two AS-OCT datasets show that our proposed FC-Net yields $$2.3\%$$ – $$8\%$$ gains on the PAS recognition performance regarding AUC, compared with the baseline models using different backbones. The code is available at https://github.com/YifYang993/FC-Net .