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Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation.
TLDR
A Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual convolutional neural Network (RRCNN), which are named RU-Net and R2U-Net respectively are proposed, which show superior performance on segmentation tasks compared to equivalent models including U-nets and residual U- net.Abstract:
Deep learning (DL) based semantic segmentation methods have been providing state-of-the-art performance in the last few years. More specifically, these techniques have been successfully applied to medical image classification, segmentation, and detection tasks. One deep learning technique, U-Net, has become one of the most popular for these applications. 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. The proposed models utilize the power of U-Net, Residual Network, as well as RCNN. There are several advantages of these proposed architectures for segmentation tasks. First, a residual unit helps when training deep architecture. Second, feature accumulation with recurrent residual convolutional layers ensures better feature representation for segmentation tasks. Third, it allows us to design better U-Net architecture with same number of network parameters with better performance for medical image segmentation. The proposed models are tested on three benchmark datasets such as blood vessel segmentation in retina images, skin cancer segmentation, and lung lesion segmentation. The experimental results show superior performance on segmentation tasks compared to equivalent models including U-Net and residual U-Net (ResU-Net).read more
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Journal ArticleDOI
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Zahangir Alom,Tarek M. Taha,Chris Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Mahmudul Hasan,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +9 more
TL;DR: This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network and goes on to cover Convolutional Neural Network, Recurrent Neural Network (RNN), and Deep Reinforcement Learning (DRL).
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The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches.
Md. Zahangir Alom,Tarek M. Taha,Christopher Yakopcic,Stefan Westberg,Paheding Sidike,Mst Shamima Nasrin,Brian Van Essen,Abdul A. S. Awwal,Vijayan K. Asari +8 more
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Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
TL;DR: This article provides a detailed review of the solutions above, summarizing both the technical novelties and empirical results, and compares the benefits and requirements of the surveyed methodologies and provides recommended solutions.
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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.
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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
References
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Book ChapterDOI
Nabla-net: A Deep Dag-Like Convolutional Architecture for Biomedical Image Segmentation
Richard McKinley,Rik Wepfer,Tom Gundersen,Franca Wagner,Andrew T. Chan,Roland Wiest,Mauricio Reyes +6 more
TL;DR: A deep convolutional architecture is reported which combines a fully-convolutional network for local features and an encoder-decoder network in which Convolutional layers and maxpooling compute high-level features, which are then upsampled to the resolution of the initial image using further convolutionAL layers and tied unpooling.
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Improved Inception-Residual Convolutional Neural Network for Object Recognition
TL;DR: The Inception Recurrent Residual Convolutional Neural Network (IRRCNN) as mentioned in this paper was proposed to improve the recognition accuracy of the Inception-residual network with same number of network parameters.
Journal ArticleDOI
Contour extraction in medical images using initial boundary pixel selection and segmental contour following
TL;DR: Experimental results show that a more detailed and accurate contour can be obtained using the proposed object contour extraction method, which has low computational complexity, which will benefit applications to clinical diagnosis, treatment, surgery, and follow up studies.