ArticleBiomedical optics express2026
DPLSeg: unsupervised general segmentation model for retinal images across multiple OCT devices.
Article in Biomedical optics express, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal diseases, including myopic and diabetic retinopathies, require early detection through precise retinal-layer segmentation in optical coherence tomography images. Existing deep-learning models generalize poorly across devices (domain shifts, noise, and high annotation costs). We propose dual-level pseudo-label learning for segmentation (DPLSeg), an unsupervised segmentation model with a dual-level pseudo-label learning strategy and a hierarchical transformer encoder to enhance feature representation and domain adaptability. Validated on 850 optical coherence tomography images from three devices, DPLSeg achieves a mean intersection over union of 79.9%, surpassing DeepLab (75.2%) and DAFormer, reducing annotation needs by 80% and providing a scalable clinical diagnostic tool.
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.