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Found 5 papers, 2 papers with code
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease.
Learning to Address Intra-segment Misclassification in Retinal Imaging
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME.
Forecasting Future Humphrey Visual Fields Using Deep Learning
Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database.
Deep learning for predicting refractive error from retinal fundus images
Mean absolute error (MAE) of the algorithm's prediction compared to the refractive error obtained in the AREDS and UK Biobank.
