Retinal vessel segmentation is the task of segmenting vessels in retina imagery.
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There is large consent that successful training of deep networks requires many thousand annotated training samples.
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
SOTA for Lung Nodule Segmentation on LUNA
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
#2 best model for Retinal Vessel Segmentation on STARE (F1 score metric)
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.
The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them.
Vessel segmentation in fundus image is a challenging task due to low contrast, the presence of microaneurysms and hemorrhages.