arXiv:2606. 15129v1 Announce Type: cross Abstract: Color fundus photography (CFP) is the mainstay for large-scale retinal screening, yet its diagnostic capacity is constrained by the lack of depth-resolved structural information.
arXiv:2602.08580v4 Announce Type: replace-cross Abstract: Automatic extraction of retinal vascular biomarkers from color fundus images (CFI) is crucial for large-scale studies of the retinal vasculat...
arXiv:2607. 05825v1 Announce Type: cross Abstract: Background.
The study introduces an explainable OCTA pipeline for Alzheimer’s disease phenotyping that combines vessel segmentation, layer‑specific biomarker extraction, and label‑free phenotyping with large language model (LLM) reporting. Using 117 images from 39 subjects, the segmentation models achieved high ROC‑AUC (0.916–0.970) and Dice scores (0.695–0.781), and six vascular biomarkers were used to create subject‑level profiles for exploratory clustering. LLMs (GPT, Gemini, Llama) produced measurement‑grounded reports evaluated for citation faithfulness and diagnostic caution, offering a transparent, non‑diagnostic link between retinal vascular data and Alzheimer’s research.
This study presents four deep‑learning pipelines—two‑dimensional and three‑dimensional—for segmenting age‑related macular degeneration (AMD) and diabetic macular edema (DME) lesions in optical coherence tomography (OCT) images. The models achieve Dice scores between 0.76 and 0.82 and demonstrate strong volumetric and surface calibration (r_vol, r_surf ≥ 0.97) on an in‑domain validation set. Generalization was assessed on the OLIVES clinical cohort using proxy metrics such as biomarker AUROC, central subfield thickness correlation, and longitudinal concordance, showing that the predictions still track clinical biomarkers outside the training distribution, albeit with reduced strength.
Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent representations and provide limited interpretabilit...
arXiv:2609.12574v1 Announce Type: new Abstract: Ultra-widefield (UWF) swept-source optical coherence tomography angiography (SS-OCTA) enables large-area retinal vascular imaging, yet vessel segmentat...
The study presents an interpretable AI framework for glaucoma screening using low-cost handheld retinal cameras in a West African population. Trained on 681 participants, the system achieved high performance across vessel segmentation, cup/disc segmentation, and optic nerve head feature detection, with classification AUCs of 0.85 for the handheld device and 0.93 for a tabletop camera. The model provides confidence scores and visual explanations to aid clinical interpretation.
arXiv:2608.24723v1 Announce Type: new Abstract: Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent re...
arXiv:2606. 16234v1 Announce Type: cross Abstract: Fundus fluorescein angiography (FFA) is critical for assessing retinal vascular abnormalities, but its acquisition is invasive and not always feasible.
arXiv:2608.31094v1 Announce Type: new Abstract: Patient identity errors can compromise longitudinal medical records, research databases, and downstream clinical decisions. We present a retinal biomet...
ARCOS is a patch-based zero-shot boundary localization framework designed for corneal layer segmentation in optical coherence tomography (OCT) images. It predicts boundary heatmaps from overlapping native-resolution patches, stitches these predictions across full B-scans, and refines them with a self-conditioned module to produce continuous, anatomically ordered layer segmentations. Evaluated on clinical OCT data from multiple devices, ARCOS achieved 95.1% off‑by‑one boundary accuracy on matched-device tests and 84.3% on unseen devices, with low mean absolute boundary errors and accurate thickness estimates.