ArticleScientific reports2023
An interpretable transformer network for the retinal disease classification using optical coherence tomography.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 93 citations in OpenAlex.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- A comprehensive overview: deep learning approaches to central serous chorioretinopathy diagnosis.BMC ophthalmology · 2025Pooled it
- Retinal imaging and supervised learning predict hospitalizations and kidney and heart-lung damage in sickle cell disease.Blood advances · 2026Observational
- OcuViT: A Vision Transformer-Based Approach for Automated Diabetic Retinopathy and AMD Classification.Journal of imaging informatics in medicine · 2026Article
- Retinal disease classification from OCT images using EfficientNetB3 with neutrosophic similarity score enhancement.Journal of applied clinical medical physics · 2026Article
- Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography.Ophthalmology science · 2026Article
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- Compact vision models match domain-specific foundation models for several retinal imaging classification tasks: A systematic benchmark.PloS one · 2026Article
- SLight-Net: a lightweight spectral-layer aware network for retinal disease detection based on optical coherence tomography.Frontiers in cell and developmental biology · 2026Article
- A two-stage deep learning framework with progressive feature fusion and attention mechanisms for multi-class retinal disease classification.Frontiers in artificial intelligence · 2026Article
- A spatial adaptive multi-scale ConvNeXt framework for robust, calibrated, and statistically validated multi-class retinal OCT classification.Frontiers in artificial intelligence · 2026Article
- Classification of Choroidal Neovascularization and Diabetic Macular Edema Based on Feature Extraction from Optical Coherence Tomography Images.Advances in experimental medicine and biology · 2026Article
- Automated retinal disease classification using deep learning and AlexNet with statistical models analysis.PloS one · 2026Article
- Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.Progress in retinal and eye research · 2026Review
- Evaluation of classification performance for six types of fundus diseases in OCT images based on multi-source training strategy.Frontiers in medicine · 2026Article
- Vision Transformers in Medical Imaging: a Comprehensive Review of Advancements and Applications Across Multiple Diseases.Journal of imaging informatics in medicine · 2025Review
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- Self-AttentionNeXt: Exploring schizophrenic optical coherence tomography image detection investigations.World journal of psychiatry · 2025Article
- Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks.Bioengineering (Basel, Switzerland) · 2025Review
- Advancements in Machine Learning for Precision Diagnostics and Surgical Interventions in Interconnected Musculoskeletal and Visual Systems.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis. In this paper, we propose an interpretable Swin-Poly Transformer network for performing automatically retinal OCT image classification. By shifting the window partition, the Swin-Poly Transformer constructs connections between neighboring non-overlapping windows in the previous layer and thus has the flexibility to model multi-scale features. Besides, the Swin-Poly Transformer modifies the importance of polynomial bases to refine cross entropy for better retinal OCT image classification. In addition, the proposed method also provides confidence score maps, assisting medical practitioners to understand the models' decision-making process. Experiments in OCT2017 and OCT-C8 reveal that the proposed method outperforms both the convolutional neural network approach and ViT, with an accuracy of 99.80% and an AUC of 99.99%.
Indexed as
Identifiers
What OpenQuestion holds
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.