Supervision by Denoising.
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Adrian V. Dalca | Christopher A. Metzler | J. E. Iglesias | Sean I. Young | P. Golland | B. Fischl | Enzo Ferrante
[1] Aydogan Ozcan,et al. Deep learning-enabled virtual histological staining of biological samples , 2022, Light: Science & Applications.
[2] G. Carneiro,et al. Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation , 2021, Computer Vision and Pattern Recognition.
[3] Dong Ni,et al. Style Curriculum Learning for Robust Medical Image Segmentation , 2021, MICCAI.
[4] Yuhui Yuan,et al. Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Jens Petersen,et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation , 2020, Nature Methods.
[6] Salman Siddique Khan,et al. FlatNet: Towards Photorealistic Scene Reconstruction From Lensless Measurements , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[7] Luc Van Gool,et al. Plug-and-Play Image Restoration With Deep Denoiser Prior , 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Enzo Ferrante,et al. Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders , 2020, IEEE Transactions on Medical Imaging.
[9] Ramesh Raskar,et al. Deep Polarization Cues for Transparent Object Segmentation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Bruce Fischl,et al. SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images , 2020, IEEE Transactions on Medical Imaging.
[11] Jason Cong,et al. SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network , 2020, IEEE Transactions on Medical Imaging.
[12] Jianming Liang,et al. UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation , 2019, IEEE Transactions on Medical Imaging.
[13] Quoc V. Le,et al. Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Ke Yan,et al. Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks , 2019, Scientific Reports.
[15] Marleen de Bruijne,et al. Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations , 2019, MICCAI.
[16] Ilkay Öksüz,et al. A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Venkateswararao Cherukuri,et al. Deep Retinal Image Segmentation With Regularization Under Geometric Priors , 2019, IEEE Transactions on Image Processing.
[18] Yang Zhao,et al. Deep High-Resolution Representation Learning for Visual Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Yifan Peng,et al. Deep Optics for Single-Shot High-Dynamic-Range Imaging , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Seong Joon Oh,et al. CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[21] David Berthelot,et al. MixMatch: A Holistic Approach to Semi-Supervised Learning , 2019, NeurIPS.
[22] Septimiu E. Salcudean,et al. Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.
[23] Huan Wang,et al. Deep Spatial–Spectral Representation Learning for Hyperspectral Image Denoising , 2019, IEEE Transactions on Computational Imaging.
[24] Xinlei Chen,et al. Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[25] Hao Chen,et al. Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[26] Adrian V. Dalca,et al. Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Frederic Cervenansky,et al. Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography , 2019, IEEE Transactions on Medical Imaging.
[28] Hongming Shan,et al. Quadratic Autoencoder (Q-AE) for Low-Dose CT Denoising , 2019, IEEE Transactions on Medical Imaging.
[29] Luc Van Gool,et al. Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] David S. Taubman,et al. Fast Optical Flow Extraction from Compressed Video , 2018, 2018 Digital Image Computing: Techniques and Applications (DICTA).
[31] Yi Yang,et al. Taking a Closer Look at Domain Shift: Category-Level Adversaries for Semantics Consistent Domain Adaptation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Wangmeng Zuo,et al. Toward Convolutional Blind Denoising of Real Photographs , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Ghassan Hamarneh,et al. Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation , 2018, MICCAI.
[34] Philip Schniter,et al. Regularization by Denoising: Clarifications and New Interpretations , 2018, IEEE Transactions on Computational Imaging.
[35] Xin Yang,et al. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? , 2018, IEEE Transactions on Medical Imaging.
[36] Andrea Vedaldi,et al. Deep Image Prior , 2017, International Journal of Computer Vision.
[37] Swami Sankaranarayanan,et al. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[38] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[39] Hariharan Ravishankar,et al. Learning and Incorporating Shape Models for Semantic Segmentation , 2017, MICCAI.
[40] Alan L. Yuille,et al. Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans , 2017, MICCAI.
[41] Konstantinos Kamnitsas,et al. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation , 2017, IEEE Transactions on Medical Imaging.
[42] Wangmeng Zuo,et al. Learning Deep CNN Denoiser Prior for Image Restoration , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Michael Möller,et al. Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[44] Harri Valpola,et al. Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.
[45] Jiawen Chen,et al. Bilateral guided upsampling , 2016, ACM Trans. Graph..
[46] Michael Elad,et al. The Little Engine That Could: Regularization by Denoising (RED) , 2016, SIAM J. Imaging Sci..
[47] Timo Aila,et al. Temporal Ensembling for Semi-Supervised Learning , 2016, ICLR.
[48] Lei Zhang,et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.
[49] Vladlen Koltun,et al. Playing for Data: Ground Truth from Computer Games , 2016, ECCV.
[50] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[51] Antonio M. López,et al. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[52] Tolga Tasdizen,et al. Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning , 2016, NIPS.
[53] Stanley H. Chan,et al. Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications , 2016, IEEE Transactions on Computational Imaging.
[54] Charles A. Bouman,et al. Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation , 2015, IEEE Transactions on Computational Imaging.
[55] Jonathan T. Barron,et al. The Fast Bilateral Solver , 2015, ECCV.
[56] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[57] Jonathan T. Barron,et al. Fast bilateral-space stereo for synthetic defocus , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[59] Thomas Pock,et al. Non-local Total Generalized Variation for Optical Flow Estimation , 2014, ECCV.
[60] Sébastien Bubeck. Convex Optimization: Algorithms and Complexity , 2014, Found. Trends Mach. Learn..
[61] Brendt Wohlberg,et al. Plug-and-Play priors for model based reconstruction , 2013, 2013 IEEE Global Conference on Signal and Information Processing.
[62] Essa Yacoub,et al. The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.
[63] Daniel P. Kennedy,et al. The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.
[64] Enhong Chen,et al. Image Denoising and Inpainting with Deep Neural Networks , 2012, NIPS.
[65] Vladlen Koltun,et al. Efficient Nonlocal Regularization for Optical Flow , 2012, ECCV.
[66] Arno Klein,et al. 101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..
[67] Bruce Fischl,et al. FreeSurfer , 2012, NeuroImage.
[68] Karl Kunisch,et al. Total Generalized Variation , 2010, SIAM J. Imaging Sci..
[69] Marc Teboulle,et al. Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.
[70] Vladimir Kolmogorov,et al. Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[71] Jack Tumblin,et al. The trilateral filter for high contrast images and meshes , 2003, Rendering Techniques.
[72] Olga Veksler,et al. Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[73] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[74] Jitendra Malik,et al. Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..
[75] Adrian V. Dalca,et al. SynthSeg: Domain Randomisation for Segmentation of Brain MRI Scans of any Contrast and Resolution , 2021, ArXiv.
[76] João Paulo Papa,et al. Semi-supervised Segmentation Based on Error-Correcting Supervision , 2020, ECCV.
[77] Li Wen,et al. Learning Semantic Segmentation From Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach , 2019 .
[78] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[79] Peyman Milanfar,et al. A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical , 2013, IEEE Signal Processing Magazine.
[80] Bruce Fischl,et al. Adaptive Nonlocal Filtering: A Fast Alternative to Anisotropic Diffusion for Image Enhancement , 1999, IEEE Trans. Pattern Anal. Mach. Intell..