Semi-Supervised Learning for Anterior Chamber Assessment: Fusing SAM with Adaptive Adapters

Ting Wang, Xiaoming Liu, Ying Zhang, Guohuan Wu, Jinshan Tang

Abstract: Accurate structural segmentation and landmark detection in anterior segment optical coherence tomography (AS-OCT) images are crucial for extracting clinical parameters that guide the diagnosis and treatment of diseases such as glaucoma. However, current mainstream algorithmic paradigms suffer from an inherent limitation: their performance improvements heavily rely on large amounts of high-quality annotations. To overcome this bottleneck, we propose a novel semi-supervised multi-task learning framework. Our framework first incorporates the powerful Segment Anything Model (SAM) image encoder to enhance the model’s general feature extraction capability. To address SAM’s adaptability issues in the medical imaging domain, we design an adaptive feature fusion adapter (AFFA) for targeted fine-tuning, thereby improving its performance on AS-OCT images. Simultaneously, our proposed synergistic feature exchange module (SFEM) enables mutual promotion between the segmentation and detection tasks. Experimental results on a local dataset demonstrate that our proposed method achieves superior performance.

External IDs:dblp:conf/smc/WangLZWT25