ArticleFrontiers in artificial intelligence2024
Transforming glaucoma diagnosis: transformers at the forefront.
Article in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 13 citations in OpenAlex.
- Learning Ophthalmologist Clinical Reasoning for Glaucoma Diagnosis from Fundus Images.medRxiv : the preprint server for health sciences · 2026Article
- A Lightweight Vision Transformer and Retinal Biomarker Fusion Framework for Early Glaucoma Detection: Toward Improved Clinical Screening.Journal of clinical medicine · 2026Article
- Explainable Transformer-Based Framework for Glaucoma Detection from Fundus Images Using Multi-Backbone Segmentation and vCDR-Based Classification.Diagnostics (Basel, Switzerland) · 2025Article
- MLG: a mixed local and global model for brain tumor classification.Frontiers in neuroscience · 2025Article
- CA-ViT: Contour-Guided and Augmented Vision Transformers to Enhance Glaucoma Classification Using Fundus Images.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although the Vision Transformer architecture has become widely accepted as the standard for image classification tasks, using it for object detection in computer vision poses significant challenges. This research aims to explore the potential of extending the Vision Transformer for object detection in medical imaging, specifically for glaucoma detection, and also includes an examination of the Detection Transformer for comparative analysis. The analysis involves assessing the cup-to-disc ratio and identifying signs of vertical thinning of the neuroretinal rim. A diagnostic threshold is proposed, flagging a cup-to-disc ratio exceeding 0.6 as a potential indicator of glaucoma. The experimental results demonstrate a remarkable 90.48% accuracy achieved by the pre-trained Detection Transformer, while the Vision Transformer exhibits competitive accuracy at 87.87%. Comparative evaluations leverage a previously untapped dataset from the Standardized Fundus Glaucoma Dataset available on Kaggle, providing valuable insights into automated glaucoma detection. The evaluation criteria and results are comprehensively validated by medical experts specializing in the field of glaucoma.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.