ASE²-ViT: An Adaptive Self-Supervised Edge-Enhanced Vision Transformer Framework for Explainable and Robust Diabetic Retinopathy Detection Using Multi-Scale Attention Learning
Keywords:
Diabetic Retinopathy Detection; Self-Supervised Learning; Vision Transformer; Explainable Artificial Intelligence; Edge-Enhanced Feature Extraction; Medical Image AnalysisAbstract
Diabetic Retinopathy (DR) is one of the most common preventable blindnesscondition around the world, where early detection of DR is necessary and requires thedevelopment of computer-aided diagnostic systems that are accurate, scalable andinterpretable. Despite success in classification, the recent deep learning models are still facing
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