187 citations · 494 across the 5 of their papers we have counts for
5 papers
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Yukun Zhou, Moucheng Xu, Yipeng Hu +5
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for bo…
Learning to Address Intra-segment Misclassification in Retinal Imaging
Yukun Zhou, Moucheng Xu, Yipeng Hu +4
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels i…
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Avinash Varadarajan, Pinal Bavishi, Paisan Raumviboonsuk +15
Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become incre…
Forecasting Future Humphrey Visual Fields Using Deep Learning
Joanne C. Wen, Cecilia S. Lee, Pearse A. Keane +5
Purpose: To determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF). Participants: All patients who obtained a HVF 24-2 at the Un…
Deep learning for predicting refractive error from retinal fundus images
Avinash V. Varadarajan, Ryan Poplin, Katy Blumer +7
Refractive error, one of the leading cause of visual impairment, can be corrected by simple interventions like prescribing eyeglasses. We trained a deep learning algorithm to predi…