Search Results for author:
Found 6 papers, 2 papers with code
Fully Automated Artery-Vein ratio and vascular tortuosity measurement in retinal fundus images
This paper is a follow-up paper on vessel topology estimation and extraction, we use the extracted topology to perform A-V state-of-the-art Artery-Vein classification, AV ratio calculation, and vessel tortuosity measurement, all fully automated.
Fully Automated Tree Topology Estimation and Artery-Vein Classification
We also performed several ablation studies to separately verify the importance of the segmentation and AV labeling steps of our proposed method.
Optic Disc Segmentation using Disk-Centered Patch Augmentation
The optic disc is a crucial diagnostic feature in the eye since changes to its physiognomy is correlated with the severity of various ocular and cardiovascular diseases.
Peering Beyond the Gradient Veil with Distributed Auto Differentiation
We introduce an innovative, communication-friendly approach for training distributed DNNs, which capitalizes on the outer-product structure of the gradient as revealed by the mechanics of auto-differentiation.
AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning
With AIP, it is trivial to capture the same image under different conditions (e. g., fidelity, lighting, etc.)
Ranked #1 on
Depth Estimation
on DIODE
Dynamic Deep Networks for Retinal Vessel Segmentation
To address this limitation, we propose a novel, stochastic training scheme for deep neural networks that better classifies the faint, ambiguous regions of the image.
