Performance comparison of convolutional neural network based denoising in low dose CT images for various loss functions
暂无分享,去创建一个
Byeongjoon Kim | Jongduk Baek | Hyunjung Shim | Minah Han | Hyunjung Shim | J. Baek | Byeongjoon Kim | Minah Han
[1] Kyle J. Myers,et al. Incorporating Human Contrast Sensitivity in Model Observers for Detection Tasks , 2009, IEEE Transactions on Medical Imaging.
[2] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[3] A. Burgess. Statistically defined backgrounds: performance of a modified nonprewhitening observer model. , 1994, Journal of the Optical Society of America. A, Optics, image science, and vision.
[4] Vincent Dumoulin,et al. Deconvolution and Checkerboard Artifacts , 2016 .
[5] Hu Chen,et al. Low-dose CT via convolutional neural network. , 2017, Biomedical optics express.
[6] R. Siddon. Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.
[7] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[8] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[9] Jiang Hsieh,et al. Computed Tomography: Principles, Design, Artifacts, and Recent Advances, Fourth Edition , 2022 .
[10] Jong Chul Ye,et al. A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction , 2016, Medical physics.
[11] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[12] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.