Search Results for author:
Found 2 papers, 1 papers with code
On the Evaluation and Real-World Usage Scenarios of Deep Vessel Segmentation for Retinography
The first focuses on the task of inference on high-resolution fundus images for which only a limited set of ground-truth data is publicly available.
M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environments
In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
