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Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images.
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TLDR
This work presents a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images using an ensemble of deep convolutional neural networks to segment vessel and non-vessel areas of a color fundus image.Abstract:
Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images. An ensemble of deep convolutional neural networks is trained to segment vessel and non-vessel areas of a color fundus image. During inference, the responses of the individual ConvNets of the ensemble are averaged to form the final segmentation. In experimental evaluation with the DRIVE database, we achieve the objective of vessel detection with maximum average accuracy of 94.7\% and area under ROC curve of 0.9283.read more
Citations
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Journal ArticleDOI
Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network
Allen Zhang,Allen Zhang,Kelvin C. P. Wang,Kelvin C. P. Wang,Baoxian Li,Enhui Yang,Xianxing Dai,Yi Peng,Yue Fei,Yang Liu,Joshua Q. Li,Cheng Chen +11 more
TL;DR: The CrackNet, an efficient architecture based on the Convolutional Neural Network, is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy.
Journal ArticleDOI
DUNet: A deformable network for retinal vessel segmentation
TL;DR: Wang et al. as discussed by the authors proposed Deformable U-Net (DUNet), which exploits the retinal vessels' local features with a U-shape architecture, in an end-to-end manner for retinal vessel segmentation.
Proceedings ArticleDOI
Weighted Res-UNet for High-Quality Retina Vessel Segmentation
TL;DR: A U-Net-like model with the weighted attention mechanism and the skip connection scheme for addressing issues of dealing with small thin vessels, low discriminative ability at the optic disk area, etc is proposed.
Journal ArticleDOI
Deep image mining for diabetic retinopathy screening.
TL;DR: In this article, a generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps, showing which pixels in images play a role in the image-level predictions.
Proceedings ArticleDOI
A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation
Avijit Dasgupta,Sonam Singh +1 more
TL;DR: In this paper, the authors formulated the segmentation task as a multi-label inference task and utilized the implicit advantages of the combination of convolutional neural networks and structured prediction.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
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Ensemble Methods in Machine Learning
TL;DR: Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.