59 citations · 128 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning
Min Gao, Yukun Guo, Tristan T. Hormel +3
Typical optical coherence tomographic angiography (OCTA) acquisition areas on commercial devices are 3x3- or 6x6-mm. Compared to 3x3-mm angiograms with proper sampling density, 6x6…