scholarly journals Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images

2013 ◽  
pp. 649-656 ◽  
Author(s):  
Guorong Wu ◽  
Minjeong Kim ◽  
Qian Wang ◽  
Yaozong Gao ◽  
Shu Liao ◽  
...  
Keyword(s):  
Feature Learning ◽  
Deformable Registration ◽  
Brain Images ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Mr Brain Images ◽  
Mr Brain

Learning-based deformable registration of MR brain images

2006 ◽  
Vol 25 (9) ◽  
pp. 1145-1157 ◽  
Author(s):  
Guorong Wu ◽  
Feihu Qi ◽  
Dinggang Shen
Keyword(s):  
Deformable Registration ◽  
Brain Images ◽  
Mr Brain Images ◽  
Mr Brain

scholarly journals Symmetric Deformable Registration via Learning a Pseudomean for MR Brain Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaodan Sui ◽  
Yuanjie Zheng ◽  
Yunlong He ◽  
Weikuan Jia
Keyword(s):  
Anatomical Variations ◽  
Deformable Registration ◽  
Deformation Field ◽  
Image Pair ◽  
Imaging Analysis ◽  
Brain Images ◽  
Image Guided ◽  
Mr Brain Images ◽  
Image Pairs ◽  
Mr Brain

Image registration is a fundamental task in medical imaging analysis, which is commonly used during image-guided interventions and data fusion. In this paper, we present a deep learning architecture to symmetrically learn and predict the deformation field between a pair of images in an unsupervised fashion. To achieve this, we design a deep regression network to predict a deformation field that can be used to align the template-subject image pair. Specifically, instead of estimating the single deformation pathway to align the images, herein, we predict two halfway deformations, which can move the original template and subject into a pseudomean space simultaneously. Therefore, we train a symmetric registration network (S-Net) in this paper. By using a symmetric strategy, the registration can be more accurate and robust particularly on the images with large anatomical variations. Moreover, the smoothness of the deformation is also significantly improved. Experimental results have demonstrated that the trained model can directly predict the symmetric deformations on new image pairs from different databases, consistently producing accurate and robust registration results.

scholarly journals Multiview Deep Feature Learning Network for SAR Automatic Target Recognition

2021 ◽  
Vol 13 (8) ◽  
pp. 1455
Author(s):  
Jifang Pei ◽  
Weibo Huo ◽  
Chenwei Wang ◽  
Yulin Huang ◽  
Yin Zhang ◽  
...  
Keyword(s):  
Target Recognition ◽  
Feature Learning ◽  
Automatic Target Recognition ◽  
Operating Conditions ◽  
Sar Images ◽  
Single View ◽  
Learning Network ◽  
Deep Feature ◽  
Deep Feature Learning ◽  
Classification Information

Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.

DFL-PiDA: Prediction of Piwi-interacting RNA-Disease Associations based on Deep Feature Learning

2021 ◽  
Author(s):  
Boya Ji ◽  
Jiawei Luo ◽  
Liangrui Pan ◽  
Xiaolan Xie ◽  
Shaoliang Peng
Keyword(s):  
Feature Learning ◽  
Deep Feature ◽  
Disease Associations ◽  
Deep Feature Learning
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