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Found 6 papers, 2 papers with code
Improve Robustness of Eye Disease Detection by including Learnable Probabilistic Discrete Latent Variables into Machine Learning Models
This study introduces a novel application of GFlowOut, leveraging the probabilistic framework of Generative Flow Networks (GFlowNets) to learn the posterior distribution over dropout masks, for the classification and analysis of ocular diseases using eye fundus images.
Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
Therefore, localization has its unique challenges different from segmentation or detection.
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.
Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
We argue this is because the vast amount of information available in a 3D OCT scan of the ONH has not been fully exploited.
Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma.
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.
