ArticleScientific reports2026
A novel superpixel based Vision Transformer for improving interpretability in glaucoma screening.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A Lightweight Vision Transformer and Retinal Biomarker Fusion Framework for Early Glaucoma Detection: Toward Improved Clinical Screening.Journal of clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Interpretability remains one of the major challenges in the clinical adoption of deep learning models for medical image analysis. In ophthalmology, particularly for glaucoma screening, explainable artificial intelligence (XAI) methods are essential for ensuring trust and diagnostic transparency. This study introduces the Superpixel-based Vision Transformer (SpxViT), a model designed to enhance interpretability while maintaining competitive accuracy. SpxViT replaces the traditional fixed grid tokenization of Vision Transformers. (ViTs) with a superpixel-based approach that preserves semantic boundaries within the retinal image. Two variants, SpxViT_fix and SpxViT_var, were evaluated on public and private glaucoma datasets. Results demonstrate that SpxViT achieves comparable accuracy to ViT-B/16 (91.9% vs. 92.5%) while producing more clinically consistent attention maps focused on the optic disc and cup.
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.