Attention to Key Fundus Features: A Prior Knowledge-Guided Deep Learning Network for Pediatric High Myopia Detection

Non-invasive fundus images can be used to diagnose various fundus diseases, such as high myopia (HM). Existing deep learning-based research mainly relies on data to drive the model to learn key features. However, the data related to HM is limited (especially for young children), making it difficult for deep networks to accurately focus on key features. Hence, we propose a prior knowledge-guided deep learning network for pediatric HM detection. It comprises four modules: (1) Prior Feature-Based Channel Fusion: This module extracts key features (brightness, edges, texture) from fundus images using image processing methods to obtain corresponding single-channel slices. Through channel-level feature fusion, these slices are used to construct multiple sets of feature-enhanced datasets. (2) Global Fundus Feature Extraction: It uses residual blocks to build the backbone, and builds aU-shaped attention component based on the U -shaped network. This module extracts the global and context information of the original fundus image to obtain a global feature map. (3) Knowledge-Guided Attention Generation: The residual structure is employed to further extract the hidden features of the feature-enhanced data, thereby obtaining local key feature maps. (4) Pediatric HM Classification: By combining local key feature maps (obtained in module 3) with global feature maps (obtained in module 2) through spatial attention mechanism, the deep network is guided to complete the classification task of pediatric HM. Extensive experiments on real-world datasets demonstrate the effectiveness of our method (accuracy is 0.921, F1 score is 0.903).