暂无分享,去创建一个
Xin Yu | Shunxing Bao | Yuankai Huo | Bennett A. Landman | Yucheng Tang | Ho Hin Lee | Qi Yang | B. Landman | Yuankai Huo | S. Bao | Xin Yu | Qi Yang | Yucheng Tang
[1] Pascal Fua,et al. Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning , 2021, ArXiv.
[2] Shunxing Bao,et al. Body Part Regression With Self-Supervision , 2021, IEEE Transactions on Medical Imaging.
[3] Seunghoon Hong,et al. Neural Contrast Enhancement of CT Image , 2021, 2021 IEEE Winter Conference on Applications of Computer Vision (WACV).
[4] Ho Hin Lee,et al. Rap-Net: Coarse-To-Fine Multi-Organ Segmentation With Single Random Anatomical Prior , 2020, 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI).
[5] Ying Wu,et al. Contrastive Learning for Label Efficient Semantic Segmentation , 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[6] Yu Zhang,et al. Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images , 2020, Pattern Recognition.
[7] Yan Wang,et al. View adaptive learning for pancreas segmentation , 2021, Biomed. Signal Process. Control..
[8] Adam P. Harrison,et al. Fully-Automated Liver Tumor Localization and Characterization from Multi-Phase MR Volumes Using Key-Slice ROI Parsing: A Physician-Inspired Approach , 2020, ArXiv.
[9] Zhangyang Wang,et al. Graph Contrastive Learning with Augmentations , 2020, NeurIPS.
[10] Ching-Yao Chuang,et al. Debiased Contrastive Learning , 2020, NeurIPS.
[11] Ertunc Erdil,et al. Contrastive learning of global and local features for medical image segmentation with limited annotations , 2020, NeurIPS.
[12] Adam P. Harrison,et al. Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation , 2020, ECCV.
[13] Stefan Roth,et al. Single-Stage Semantic Segmentation From Image Labels , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Ce Liu,et al. Supervised Contrastive Learning , 2020, NeurIPS.
[15] In So Kweon,et al. Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Vicente Grau,et al. A Deep learning Approach to Generate Contrast-Enhanced Computerised Tomography Angiography without the Use of Intravenous Contrast Agents , 2020, ArXiv.
[17] Hyeran Byun,et al. Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[19] Daguang Xu,et al. Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation , 2020, IEEE Transactions on Medical Imaging.
[20] Hao Chen,et al. Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation , 2020, IEEE Transactions on Medical Imaging.
[21] Pheng Ann Heng,et al. Unpaired Multi-Modal Segmentation via Knowledge Distillation , 2020, IEEE Transactions on Medical Imaging.
[22] Laurens van der Maaten,et al. Self-Supervised Learning of Pretext-Invariant Representations , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Ross B. Girshick,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Xilin Chen,et al. Object-Contextual Representations for Semantic Segmentation , 2019, ECCV.
[25] Phillip Isola,et al. Contrastive Multiview Coding , 2019, ECCV.
[26] Ali Razavi,et al. Data-Efficient Image Recognition with Contrastive Predictive Coding , 2019, ICML.
[27] Ming Dong,et al. Cardiac Substructure Segmentation with Deep Learning for Improved Cardiac Sparing. , 2019, Medical physics.
[28] Yang Wang,et al. Region Mutual Information Loss for Semantic Segmentation , 2019, NeurIPS.
[29] Yuanyuan Wang,et al. The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN , 2019, MICCAI.
[30] R Devon Hjelm,et al. Learning Representations by Maximizing Mutual Information Across Views , 2019, NeurIPS.
[31] Han Zhang,et al. Co-Occurrent Features in Semantic Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Yan Huang,et al. Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Xinlei Chen,et al. Prior-Aware Neural Network for Partially-Supervised Multi-Organ Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[34] Sungroh Yoon,et al. FickleNet: Weakly and Semi-Supervised Semantic Image Segmentation Using Stochastic Inference , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Nima Tajbakhsh,et al. Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).
[36] Marleen de Bruijne,et al. Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT , 2019, European Radiology.
[37] Paul Babyn,et al. Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..
[38] Yoshua Bengio,et al. Learning deep representations by mutual information estimation and maximization , 2018, ICLR.
[39] Alan L. Yuille,et al. Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma , 2018, MICCAI.
[40] Hao Chen,et al. PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation , 2018, ArXiv.
[41] Oriol Vinyals,et al. Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.
[42] Yuichiro Hayashi,et al. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation , 2018, MICCAI.
[43] Wenyu Liu,et al. Weakly-Supervised Semantic Segmentation Network with Deep Seeded Region Growing , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[44] Stella X. Yu,et al. Unsupervised Feature Learning via Non-parametric Instance Discrimination , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[45] Stella X. Yu,et al. Adaptive Affinity Fields for Semantic Segmentation , 2018, ECCV.
[46] Piotr J. Slomka,et al. Deep Learning for Quantification of Epicardial and Thoracic Adipose Tissue From Non-Contrast CT , 2018, IEEE Transactions on Medical Imaging.
[47] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[48] Klaus H. Maier-Hein,et al. Exploiting the potential of unlabeled endoscopic video data with self-supervised learning , 2017, International Journal of Computer Assisted Radiology and Surgery.
[49] Sergey Levine,et al. Time-Contrastive Networks: Self-Supervised Learning from Video , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[50] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[51] Ben Glocker,et al. DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images , 2017, ArXiv.
[52] Andrew Zisserman,et al. Self-supervised Learning for Spinal MRIs , 2017, DLMIA/ML-CDS@MICCAI.
[53] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[54] Wiro J Niessen,et al. Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans , 2017, Physics in medicine and biology.
[55] Xiaogang Wang,et al. Pyramid Scene Parsing Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[56] Alexei A. Efros,et al. Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Bernt Schiele,et al. Simple Does It: Weakly Supervised Instance and Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[58] Rama Chellappa,et al. Gaussian Conditional Random Field Network for Semantic Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[60] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[61] Alexei A. Efros,et al. Colorful Image Colorization , 2016, ECCV.
[62] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[63] George Papandreou,et al. Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[64] Alexei A. Efros,et al. Unsupervised Visual Representation Learning by Context Prediction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[65] Heinz Handels,et al. Multi-modal Multi-Atlas Segmentation using Discrete Optimisation and Self-Similarities , 2015, VISCERAL Challenge@ISBI.
[66] Subhransu Maji,et al. Semantic contours from inverse detectors , 2011, 2011 International Conference on Computer Vision.
[67] Noriyuki Moriyama,et al. Improvement of image quality of low radiation dose abdominal CT by increasing contrast enhancement. , 2010, AJR. American journal of roentgenology.
[68] Franz Pfeiffer,et al. Toward Clinical X-ray Phase-Contrast CT: Demonstration of Enhanced Soft-Tissue Contrast in Human Specimen , 2010, Investigative radiology.
[69] Luc Van Gool,et al. The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.