| Title |
Transformer-based deep learning for population-scale retinal image screening of ophthalmic disorders |
| Authors |
Abdelbaki, Wiem ; Bouchelligua, Wided ; Nasir, Inzamam Mashood ; Tehsin, Sara ; Alshaya, Hend |
| DOI |
10.3390/bioengineering13040377 |
| Full Text |
|
| Is Part of |
Bioengineering.. Basel : MDPI. 2026, vol. 13, iss. 4, art. no. 377, p. 1-39.. ISSN 2306-5354 |
| Keywords [eng] |
diabetic retinopathy ; medical image analysis ; multi-disease classification ; population-scale healthcare ; retinal image screening ; vision transformers |
| Abstract [eng] |
To perform screening of the retina on a population scale, an automated procedure is required that incorporates accurate, reproducible, interpretable, and computationally costeffective models. Existing approaches using convolutional or transformer architectures typically do not adequately represent both fine-grained pathology and large-scale retinal context simultaneously, which could adversely affect their reliability if used for large-scale applications in clinical practice. In this paper, we propose a hierarchical transformer-based screening framework for retinal fundus images that incorporates patch-based tokenization, global transformer encoding, and hierarchical aggregation of contextual information. We also developed a lightweight prediction head that supports screening for both single and multiple diseases. The framework has been evaluated using standard screening metrics, robustness, and cross-dataset generalization analyses on two eye retinopathy image databases: EyePACS and RFMiD. With regard to screening for a binary outcome of diabetic retinopathy, our method provided an accuracy of 89.4% and an area under the receiver operating characteristic (AUROC) curve of 93.6% on EyePACS and attained an accuracy of 95.2% and a macro-averaged F1 score of 82.7% on RFMiD. Our hierarchical transformer achieved improved robustness to degraded images and increased generalizability across datasets compared with all current state-of-the-art models. The proposed hierarchical transformer demonstrates strong potential for large-scale retinal screening and provides a promising foundation for future clinically validated deployment. |
| Published |
Basel : MDPI |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
