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20202025
most citedAutomated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning

59 citations · 67 across the 7 of their papers we have counts for

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5 papers · 1 filter

eess.IV2025

Robust Detection of Retinal Neovascularization in Widefield Optical Coherence Tomography

Jinyi Hao, Jie Wang, Liqin Gao +8

Retinal neovascularization (RNV) is a vision threatening development in diabetic retinopathy (DR). Vision loss associated with RNV is preventable with timely intervention, making R…

eess.IV2022★ 3 cited

Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map

Pengxiao Zang, Tristan T. Hormel, Jie Wang +6

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCT…

eess.IV2020★ 2 cited

Focal Loss Analysis of Nerve Fiber Layer Reflectance for Glaucoma Diagnosis

Ou Tan, Liang Liu, Qisheng You +6

Purpose: To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis. Methods: Participants were imaged with 4.5X4.5-mm volumetric disc scans using spectral-domain optic…

eess.IV2020★ 59 cited

Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning

Yukun Guo, Tristan T. Hormel, Honglian Xiong +3

Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomograp…

eess.IV2020★ 3 cited

DcardNet: Diabetic Retinopathy Classification at Multiple Levels Based on Structural and Angiographic Optical Coherence Tomography

Pengxiao Zang, Liqin Gao, Tristan T. Hormel +4

Objective: Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages for the early detection and diagnosis of diabetic retinopathy (DR). However, automa…