ArticleComputers in biology and medicine2023
Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans.
Article in Computers in biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Deep Learning in Classifying Age-Related Macular Degeneration From Images: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
- Sparse-Observation Multi-Horizon Glaucoma Progression Forecasting with Biologically Constrained Temporal Consistency: A Glaucoma Case Study.Research square · 2026Article
- Outer retinal band segmentation in healthy subjects: comparative study between human grading and deep convolutional neural networks.Quantitative imaging in medicine and surgery · 2026Article
- Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis.Translational vision science & technology · 2026Article
- DPLSeg: unsupervised general segmentation model for retinal images across multiple OCT devices.Biomedical optics express · 2026Article
- Clinically informed semi-supervised learning improves disease annotation and equity from electronic health records: a glaucoma case study.NPJ digital medicine · 2025Article
- Interpretable multi-model deep learning framework for automated four-class diagnosis of ocular toxoplasmosis using fundus imaging.Scientific reports · 2025Article
- A robust deep learning classifier for screening multiple retinal diseases on optical coherence tomography.Scientific reports · 2025Article
- The Impact of Informal Digital Learning of English (IDLE) on EFL Learners' Engagement: Mediating Roles of Flow, Online Self-Efficacy, and Behavioral Intention.Behavioral sciences (Basel, Switzerland) · 2025Article
- Advanced 3D retinal lesion segmentation using channel-spatial attention-guided multi-scale feature aggregation.Biomedical optics express · 2025Article
- PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data.Medicina (Kaunas, Lithuania) · 2025Article
- Retina Blood Vessels Segmentation and Classification with the Multi-featured Approach.Journal of imaging informatics in medicine · 2025Article
- Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique.Scientific reports · 2025Article
- A novel approach for automatic classification of macular degeneration OCT images.Scientific reports · 2024Article
- Self-attention CNN for retinal layer segmentation in OCT.Biomedical optics express · 2024Article
- High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention.Biomedical optics express · 2024Article
- Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images.PloS one · 2024Article
- Cost-efficient and glaucoma-specifical model by exploiting normal OCT images with knowledge transfer learning.Biomedical optics express · 2023Article
- Wavelet scattering transform application in classification of retinal abnormalities using OCT images.Scientific reports · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundAccurate retinal layer segmentation in optical coherence tomography (OCT) images is crucial for quantitatively analyzing age-related macular degeneration (AMD) and monitoring its progression. However, previous retinal segmentation models depend on experienced experts and manually annotating retinal layers is time-consuming. On the other hand, accuracy of AMD diagnosis is directly related to the segmentation model's performance. To address these issues, we aimed to improve AMD detection using optimized retinal layer segmentation and deep ensemble learning.
methodWe integrated a graph-cut algorithm with a cubic spline to automatically annotate 11 retinal boundaries. The refined images were fed into a deep ensemble mechanism that combined a Bagged Tree and end-to-end deep learning classifiers. We tested the developed deep ensemble model on internal and external datasets.
resultsThe total error rates for our segmentation model using the boundary refinement approach was significantly lower than OCT Explorer segmentations (1.7% vs. 7.8%, p-value = 0.03). We utilized the refinement approach to quantify 169 imaging features using Zeiss SD-OCT volume scans. The presence of drusen and thickness of total retina, neurosensory retina, and ellipsoid zone to inner-outer segment (EZ-ISOS) thickness had higher contributions to AMD classification compared to other features. The developed ensemble learning model obtained a higher diagnostic accuracy in a shorter time compared with two human graders. The area under the curve (AUC) for normal vs. early AMD was 99.4%.
conclusionTesting results showed that the developed framework is repeatable and effective as a potentially valuable tool in retinal imaging research.
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