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Efficient Vision Transformers for Ophthalmic Images Classification: A Comparative Study of Supervised,
Ahmed Shakir Al-Wassiti1, Mohammed Tareq Mutar2, Ahmed Sermed Al Sakini3
1MBChB, FIBMS (ophthalmology), FICO, FRCS (Glasg), College of Medicine, University of Baghdad, Baghdad, Baghdad Governorate, Iraq.
Journal of Medical Systems
|November 16, 2025
Summary
This study enhances ophthalmic image classification using AI, combining supervised, semi-supervised, and unsupervised learning to improve diagnostics with minimal labeled data. MaxViT-L shows promising performance, balancing accuracy and generalization for automated eye disease detection.
Area of Science:
- Ophthalmology
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Medical imaging, particularly in ophthalmology, faces challenges with high annotation costs, limiting the development of AI diagnostic tools.
- Supervised learning models require extensive labeled data, which is scarce and expensive to obtain in specialized fields like ophthalmic imaging.
- Developing robust diagnostic systems necessitates methods that can perform effectively even with limited labeled datasets.
Purpose of the Study:
- To investigate the integration of supervised, semi-supervised, and unsupervised learning strategies for classifying ophthalmic images under label-scarce conditions.
- To enhance diagnostic performance in ophthalmology by leveraging minimal labeled data and robust feature representations.
- To evaluate the effectiveness of different transformer architectures and learning strategies in improving AI-driven ophthalmic diagnostics.
Main Methods:
- Utilized a dataset of 18,767 multimodal ophthalmic images (1,877 labeled, 16,890 unlabeled).
- Employed supervised learning with ViT-Base, DeiT-Base, and MaxViT-L transformer architectures.
- Implemented semi-supervised learning via pseudo-labeling (confidence threshold ≥ 0.98) and unsupervised learning using SimCLR-based contrastive learning and K-means clustering.
Main Results:
- In supervised learning, ViT-Base achieved 92.47% accuracy. After semi-supervised pseudo-labeling, MaxViT-L reached 97.49% accuracy and 0.9982 AUC.
- Unsupervised contrastive learning with MaxViT-L improved feature clustering (Silhouette Score: 0.556, DBI: 0.541).
- MaxViT-L demonstrated superior performance on an external validation set, offering a favorable trade-off between accuracy and generalization despite higher computational complexity.
Conclusions:
- MaxViT-L, particularly in semi- and unsupervised settings, provides a strong balance between diagnostic performance and model generalization for ophthalmic image classification.
- The integrated approach effectively minimizes reliance on expert annotations, paving the way for scalable and automated ophthalmic diagnostic solutions.
- This study highlights the potential of advanced machine learning techniques to overcome data scarcity challenges in medical AI, improving diagnostic capabilities in ophthalmology.
