Automatic Windowing for MRI With Convolutional Neural Network
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Tao Zhang | Xiaole Zhao | Hangfei Liu | Gaiyan Zhu | Xueming Zou | Tao Zhang | Xueming Zou | G. Zhu | Xiaole Zhao | Hangfei Liu
[1] Dumitru Erhan,et al. Scalable, High-Quality Object Detection , 2014, ArXiv.
[2] Alberto Corbi,et al. Automatic intensity windowing of mammographic images based on a perceptual metric , 2017, Medical physics.
[3] Yun Fu,et al. Image Super-Resolution Using Very Deep Residual Channel Attention Networks , 2018, ECCV.
[4] Yun Fu,et al. Residual Dense Network for Image Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Tom Kimpe,et al. Increasing the Number of Gray Shades in Medical Display Systems—How Much is Enough? , 2007, Journal of Digital Imaging.
[6] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[7] Mingxuan Sun,et al. Dilated Deep Residual Network for Image Denoising , 2017, 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).
[8] Xiaogang Wang,et al. DeepID3: Face Recognition with Very Deep Neural Networks , 2015, ArXiv.
[9] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[10] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[11] Eirikur Agustsson,et al. NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[12] Daniel Rueckert,et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Kyoung Mu Lee,et al. Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[14] Shinichi Yamada,et al. Automatic adjustment of display window (gray level) for MR images using a neural network , 1991, Medical Imaging.
[15] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[16] Risheng Huang,et al. RepGN: Object Detection with Relational Proposal Graph Network , 2019, ArXiv.
[17] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[18] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Quoc V. Le,et al. Attention Augmented Convolutional Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[20] Richard E. Wendt,et al. Automatic adjustment of contrast and brightness of magnetic resonance images , 1994, Journal of Digital Imaging.
[21] Garrett T. Kenyon,et al. Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks , 2017, ArXiv.
[22] R Fahrig,et al. Dynamic range requirements in digital mammography. , 1993, Medical physics.
[23] Tao Zhang,et al. Channel Splitting Network for Single MR Image Super-Resolution , 2018, IEEE Transactions on Image Processing.
[24] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[25] Yongpei Zhu,et al. Facial Expression Recognition Research Based on Deep Learning , 2019, ArXiv.
[26] Kyoung Mu Lee,et al. Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Shang-Hong Lai,et al. An adaptive window width/center adjustment system with online training capabilities for MR images , 2005, Artif. Intell. Medicine.
[28] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[29] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Xiaoou Tang,et al. Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[31] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[32] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[33] Xiaogang Wang,et al. DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection , 2014, ArXiv.
[34] Vishal Monga,et al. Adaptive Transform Domain Image Super-Resolution via Orthogonally Regularized Deep Networks , 2019, IEEE Transactions on Image Processing.
[35] Shang-Hong Lai,et al. A hierarchical neural network algorithm for robust and automatic windowing of MR images , 2000, Artif. Intell. Medicine.
[36] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[37] Xiaogang Wang,et al. Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.
[38] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[39] H. Sebastian Seung,et al. Natural Image Denoising with Convolutional Networks , 2008, NIPS.
[40] M. Y. Mashor,et al. Local and Global Contrast Stretching For Color Contrast Enhancement on Ziehl-Neelsen Tissue Section Slide Images , 2008 .