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U-Net has been providing state-of-the-art performance in many medical image segmentation problems.
“Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification,”
João VB Soares et al., · 2006
Earlier work this paper cites.
“Imagenet: A large-scale hierarchical image database,”
Jia Deng et al., · 2009
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky et al., · 2012
Earlier work this paper cites.
“Deep learning,”
Yann LeCun et al., · 2015
Earlier work this paper cites.
“Fully convolutional networks for semantic segmentation,”
Jonathan Long et al., · 2015
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation,”
Olaf Ronneberger et al., · 2015
Cited alongside, same era.
“Blood vessel segmentation of fundus images by major vessel extraction and subimage classification,”
Sohini Roychowdhury et al., · 2015
Cited alongside, same era.
“Residual networks behave like ensembles of relatively shallow networks,”
Andreas Veit et al., · 2016
Cited alongside, same era.
“Deep residual learning for image recognition,”
Kaiming He et al., · 2016
Cited alongside, same era.
“A cross-modality learning approach for vessel segmentation in retinal images.,”
Qiaoliang Li et al., · 2016
Cited alongside, same era.
“Pyramid scene parsing network,”
Hengshuang Zhao et al., · 2017
Later among the works it cites.
“The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation,”
Simon Jégou et al., · 2017
Later among the works it cites.
“Inception recurrent convolutional neural network for object recognition,”
Md Zahangir Alom et al., · 2017
Later among the works it cites.
“Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,”
Liang-Chieh Chen et al., · 2018
Closest in time.
“Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation,”
Md Zahangir Alom et al., · 2018
Closest in time.
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