Adversarial learning for deformable registration of brain MR image using a multi-scale fully convolutional network

2019 ◽  
Vol 53 ◽  
pp. 101562 ◽  
Author(s):  
Luwen Duan ◽  
Gang Yuan ◽  
Lun Gong ◽  
Tianxiao Fu ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Deformable Registration ◽  
Mr Image ◽  
Convolutional Network ◽  
Adversarial Learning ◽  
Fully Convolutional Network ◽  
Multi Scale

scholarly journals Fully Convolutional Network for Consistent Voxel-Wise Correspondence

2020 ◽  
Vol 34 (07) ◽  
pp. 12935-12942 ◽  
Author(s):  
Yungeng Zhang ◽  
Yuru Pei ◽  
Yuke Guo ◽  
Gengyu Ma ◽  
Tianmin Xu ◽  
...  
Keyword(s):  
Vector Fields ◽  
State Of The Art ◽  
Displacement Vector ◽  
Cone Beam Ct ◽  
Deformable Registration ◽  
Cone Beam ◽  
Nonlinear Mapping ◽  
Convolutional Network ◽  
Mapping Functions ◽  
Fully Convolutional Network

In this paper, we propose a fully convolutional network-based dense map from voxels to invertible pair of displacement vector fields regarding a template grid for the consistent voxel-wise correspondence. We parameterize the volumetric mapping using a convolutional network and train it in an unsupervised way by leveraging the spatial transformer to minimize the gap between the warped volumetric image and the template grid. Instead of learning the unidirectional map, we learn the nonlinear mapping functions for both forward and backward transformations. We introduce the combinational inverse constraints for the volumetric one-to-one maps, where the pairwise and triple constraints are utilized to learn the cycle-consistent correspondence maps between volumes. Experiments on both synthetic and clinically captured volumetric cone-beam CT (CBCT) images show that the proposed framework is effective and competitive against state-of-the-art deformable registration techniques.

A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection

2020 ◽  
pp. 1-13
Author(s):  
Peiyu Yang ◽  
Guofeng Zhang ◽  
Lu Wang ◽  
Lisheng Xu ◽  
Qingxu Deng ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale

scholarly journals FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security

2019 ◽  
Vol 9 (10) ◽  
pp. 2042 ◽  
Author(s):  
Rachida Tobji ◽  
Wu Di ◽  
Naeem Ayoub
Keyword(s):  
Neural Networks ◽  
Iris Recognition ◽  
State Of The Art ◽  
Features Extraction ◽  
Iris Segmentation ◽  
Recognition Method ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale ◽  
Biometric Security

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

scholarly journals Multi-Scale Fully Convolutional Network for Fast Face Detection

2016 ◽  
Author(s):  
Yancheng Bai ◽  
Wenjing Ma ◽  
Yucheng Li ◽  
Liangliang Cao ◽  
Wen Guo ◽  
...  
Keyword(s):  
Face Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale
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