Journal ArticleDOI
SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation.
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TLDR
Wang et al. as discussed by the authors proposed a novel scale and context sensitive network (a.k.a., SCS−Net) for retinal vessel segmentation, which dynamically adjusts the receptive fields to extract multi-scale features.About:
This article is published in Medical Image Analysis.The article was published on 2021-03-04. It has received 67 citations till now. The article focuses on the topics: Context (language use) & Segmentation.read more
Citations
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Journal ArticleDOI
Retinal Vessel Segmentation Using Deep Learning: A Review
TL;DR: A comprehensive review of retinal blood vessel segmentation based on deep learning is presented in this article, where the authors surveyed the proposed methods especially the network architectures and figured out the trend of models.
Journal ArticleDOI
FIVES: A Fundus Image Dataset for Artificial Intelligence based Vessel Segmentation
Kai Jin,Xingru Huang,Jingxing Zhou,Yunxiang Li,Yan Yan,Yibao Sun,Qianni Zhang,Yaqi Wang,Juan Juan. Ye +8 more
TL;DR: The FIVES dataset as discussed by the authors consists of 800 high-resolution multi-disease color fundus photographs with pixelwise manual annotation, and the annotation process was standardized through crowdsourcing among medical experts.
Journal ArticleDOI
Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation
TL;DR: Wang et al. as mentioned in this paper proposed a novel deep network architecture named bridge-net to make use of the context of the retinal blood vessels efficiently, which incorporates a recurrent neural network (RNN) into a convolutional neural network(CNN) to deliver the context and then to produce the probability map.
Journal ArticleDOI
FIVES: A Fundus Image Dataset for Artificial Intelligence based Vessel Segmentation
Kai Jin,Xingru Huang,Jingxing Zhou,Yunxiang Li,Yan Yan,Yibao Sun,Qianni Zhang,Yaqi Wang,Jun Ye +8 more
TL;DR: The FIVES dataset as mentioned in this paper consists of 800 high-resolution multi-disease color fundus photographs with pixelwise manual annotation, and the annotation process was standardized through crowdsourcing among medical experts.
Journal ArticleDOI
PCAT-UNet: UNet-like network fused convolution and transformer for retinal vessel segmentation
TL;DR: The experimental results show that the proposed PCAT-UNET method achieves good retinal vessel segmentation performance on these three datasets, and is superior to other architectures in terms of AUC, Accuracy and Sensitivity performance indicators.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Journal ArticleDOI
Squeeze-and-Excitation Networks
TL;DR: This work proposes a novel architectural unit, which is term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels and finds that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost.
Proceedings Article
Rectified Linear Units Improve Restricted Boltzmann Machines
Vinod Nair,Geoffrey E. Hinton +1 more
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
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