Abstract
Interpretability remains one of the major challenges in the clinical adoption of deep learning models for medical image analysis. In ophthalmology, particularly for glaucoma screening, explainable artificial intelligence (XAI) methods are essential for ensuring trust and diagnostic transparency. This study introduces the Superpixel-based Vision Transformer (SpxViT), a model designed to enhance interpretability while maintaining competitive accuracy. SpxViT replaces the traditional fixed grid tokenization of Vision Transformers. (ViTs) with a superpixel-based approach that preserves semantic boundaries within the retinal image. Two variants, SpxViT_fix and SpxViT_var, were evaluated on public and private glaucoma datasets. Results demonstrate that SpxViT achieves comparable accuracy to ViT-B/16 (91.9% vs. 92.5%) while producing more clinically consistent attention maps focused on the optic disc and cup.