Vision Transformers for the Segmentation of Patterns Associated to Pathology in Diabetic Retinopathy: A Literature Review and Clinical Relevance in Eye Fundus Images

This publication has 25 references indexed in Scilit:

• Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review
  Journal of Medical Systems, 2024
• A Novel Automated System for Early Diabetic Retinopathy Detection and Severity Classification
  Journal of Chemometrics, 2024
• Diabetic retinopathy prediction based on vision transformer and modified capsule network
  Computers in Biology and Medicine, 2024
• CRA-Net: Transformer guided category-relation attention network for diabetic retinopathy grading
  Computers in Biology and Medicine, 2024
• Improved ResNet_101 assisted attentional global transformer network for automated detection and classification of diabetic retinopathy disease
  Biomedical Signal Processing and Control, 2023
• Segmentation Using the IC2T Model and Classification of Diabetic Retinopathy Using the Rock Hyrax Swarm-Based Coordination Attention Mechanism
  IEEE Access, 2023
• Vision Transformer Model for Predicting the Severity of Diabetic Retinopathy in Fundus Photography-Based Retina Images
  IEEE Access, 2023
• CoT-XNet: contextual transformer with Xception network for diabetic retinopathy grading
  Physics in Medicine & Biology, 2022
• Vision Transformer‐based recognition of diabetic retinopathy grade
  Medical Physics, 2021
• Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales
  Ophthalmology, 2003