SABPI-Net: A Structure-Aware Bidirectional Proxy Interaction Network for Infantile Retinal Disease Diagnosis.

Abstract

Delayed treatment of infantile retinal disease can reduce its effectiveness and may cause severe and irreversible damage. Automated diagnosis of infant retinal diseases faces challenges including subtle early lesions, diverse clinical phenotypes, imaging variations, and imbalanced data. To address these, which cannot be well addressed by existing general foundation models, we propose structure-aware bidirectional proxy interaction network (SABPI-Net) in a universal learning framework. SABPI-Net incorporates a high-frequency mapping branch, and employs a proposed proxy interaction attention module to enable effective interaction between its trunk feature encoding branch and the high-frequency mapping branch, thereby facilitating enhanced perception of retinal detail structures. Domain-agnostic embedding space self-matching, guided by a memory-bank low-frequency component replacement strategy, promotes domain-invariant learning and consistent model performance under diverse image styles. Finally, the tail-aware feature fusion strategy for fine-tuning further enhances the model's diagnostic sensitivity to tailed diseases. In this study, three classification tasks related to infant retinal diseases are implemented on the largest clinical infant retina dataset to date, covering 19 infant retinal diseases or normal conditions. SABPI-Net achieves superior performance compared to 13 SOTA methods, with 95.32% accuracy on mainstream clinical tasks, 73.58% on ROP five-stage classification, and 84.25% on multi-disease classification, representing improvements of 1.57%, 1.88%, and 4.71% respectively over the best competing methods. Extensive experiments demonstrate the effectiveness and superiority of SABPI-Net in diagnosing infant retinal diseases.