A Knowledge-Guided Bi-Modal Network for the Classification of Anterior Chamber Angle Images

Glaucoma is a leading cause of irreversible blindness globally. When glaucoma is diagnosed, Anterior Chamber Angle (ACA) evaluation is the necessary step for the prognosis and treatment of glaucoma. However, current clinical evaluation methods are labor intensive and rely on expert judgment, which makes them inefficient. Automating ACA classification based on images using machine learning, especially deep neural networks, holds promise. Yet, image samples alone can't provide sufficient high-level semantic information on ACA, resulting in suboptimal classification performance. This paper proposes a novel end-to-end knowledge-guided bi-modal network (KGNet) for ACA evaluation. Specifically, we consider two modalities of ACA data: textual domain knowledge and images. We first design a new strategy to refine class-based knowledge into textual descriptions, thereby increasing the diversity of features learned by the model. We then extract two types of representations using two distinct components: 1) a supervised loss is applied to learn modality-specific representations by incorporating domain knowledge; 2) a fusion module that uses knowledge-guided learning to highlight key clinical structures in ACA images leveraging bimodal correlations. Experimental results on an ACA dataset and three public datasets show that our method outperforms several state-of-the-art deep learning models in eye image evaluation, indicating the potential medical interest of our method. Furthermore, our approach improves interpretability by explicitly aligning visual representations with structured clinical knowledge, enabling more structured and clinically grounded explanations than conventional models.