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Found 6 papers, 0 papers with code
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding
Our method of semantically meaningful image augmentation for object detection via language grounding, SemAug, starts by calculating semantically appropriate new objects that can be placed into relevant locations in the image (the what and where problems).
Microvasculature Segmentation and Inter-capillary Area Quantification of the Deep Vascular Complex using Transfer Learning
The inter-rater comparisons for the SCP had an accuracy and Dice index of 0. 8300 and 0. 6700, respectively, and 0. 6874 and 0. 7416 for the DVC.
Cascade Dual-branch Deep Neural Networks for Retinal Layer and fluid Segmentation of Optical Coherence Tomography Incorporating Spatial Prior
Optical coherence tomography (OCT) is a non-invasive imaging technology that can provide micrometer-resolution cross-sectional images of the inner structures of the eye.
Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye.
Deep learning vessel segmentation and quantification of the foveal avascular zone using commercial and prototype OCT-A platforms
This study demonstrates the utility of deep neural networks for automatic quantification of foveal avascular zone (FAZ) parameters and perifoveal vessel density of OCT-A images in healthy and diabetic eyes.
Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network
As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures.
