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Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma
Discussion: We provide a proof-of-principle for the application of geometric deep learning in the field of glaucoma.
Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning
In this paper, we propose an explainable and interpretable diabetic retinopathy (ExplainDR) classification model based on neural-symbolic learning.
The Three-Dimensional Structural Configuration of the Central Retinal Vessel Trunk and Branches as a Glaucoma Biomarker
Our work also suggested that the major retinal blood vessels form a skeleton -- the configuration of which may be representative of major ONH structural changes as typically observed with the development and progression of glaucoma.
DeshadowGAN: A Deep Learning Approach to Remove Shadows from Optical Coherence Tomography Images
Image quality was assessed qualitatively (for artifacts) and quantitatively using the intralayer contrast: a measure of shadow visibility ranging from 0 (shadow-free) to 1 (strong shadow) and compared to compensated images.
Deep Learning Algorithms to Isolate and Quantify the Structures of the Anterior Segment in Optical Coherence Tomography Images
Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle closure glaucoma.
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
For all the ONH tissues, the mean CNR increased from $3. 50 \pm 0. 56$ (single-frame) to $7. 63 \pm 1. 81$ (denoised).
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology.
A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head
Purpose: To develop a deep learning approach to digitally-stain optical coherence tomography (OCT) images of the optic nerve head (ONH).
