Search Results for author:
Found 8 papers, 1 papers with code
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images
Using active learning, we created a new DFI dataset containing 240 crowd-sourced manual A/V segmentations performed by fifteen medical students and reviewed by an ophthalmologist, and developed LUNet, a novel deep learning architecture for high resolution A/V segmentation.
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations
Discussion and future work: We will use active learning strategies to continue enlarging our retinal fundus dataset by including a more efficient process to select the images to be annotated and distribute them to annotators.
PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation
Results: We built a fully automated vasculature biomarker toolbox based on DFI segmentations and provided a proof of usability to characterize the vascular changes in glaucoma.
FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading
We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale.
Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review
The automatic segmentation of optic nerve head in OCT scans could further improve the current clinical management of glaucoma and other diseases.
Pointwise visual field estimation from optical coherence tomography in glaucoma: a structure-function analysis using deep learning
Conclusion: DL on unsegmented OCT scans accurately predicts pointwise and mean deviation of 24-2 VF in glaucoma patients.
Deep learning on fundus images detects glaucoma beyond the optic disc
We trained and evaluated deep learning models using fundus images that underwent a certain cropping policy.
Pathological myopia classification with simultaneous lesion segmentation using deep learning
This investigation reports on the results of convolutional neural networks developed for the recently introduced PathologicAL Myopia (PALM) dataset, which consists of 1200 fundus images.
