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Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
Over the past decade, Deep Convolutional Neural Networks have been widel...
02/21/2021 ∙ by Jeya Maria Jose Valanarasu, et al. ∙
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U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical...
11/02/2020 ∙ by Nahian Siddique, et al. ∙
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Hierarchical Attention Networks for Medical Image Segmentation
The medical image is characterized by the inter-class indistinction, hig...
11/20/2019 ∙ by Fei Ding, et al. ∙
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Deep Learning for Automated Medical Image Analysis
Medical imaging is an essential tool in many areas of medical applicatio...
03/12/2019 ∙ by Wentao Zhu, et al. ∙
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DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation
Recently, deep learning has become much more popular in computer vision ...
05/31/2020 ∙ by Ange Lou, et al. ∙
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Multiple Abnormality Detection for Automatic Medical Image Diagnosis Using Bifurcated Convolutional Neural Network
Automating classification and segmentation process of abnormal regions i...
09/16/2018 ∙ by Mohsen Hajabdollahi, et al. ∙
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Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images
Segmentation maps of medical images annotated by medical experts contain...
06/07/2019 ∙ by Yizhe Zhang, et al. ∙
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CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging
10/15/2020 ∙ by Lei Mou, et al. ∙
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Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in several 2D/3D medical imaging modalities. We introduce a new curvilinear structure segmentation network (CS2-Net), which includes a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures. Two types of attention modules - spatial attention and channel attention - are utilized to enhance the inter-class discrimination and intra-class responsiveness, to further integrate local features with their global dependencies and normalization, adaptively. Furthermore, to facilitate the segmentation of curvilinear structures in medical images, we employ a 1x3 and a 3x1 convolutional kernel to capture boundary features. ...
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