Controllable cardiac synthesis via disentangled anatomy arithmetic
暂无分享,去创建一个
Sotirios A. Tsaftaris | Alison Q. O'Neil | Spyridon Thermos | Xiao Liu | Alison O'Neil | Spyridon Thermos | S. Tsaftaris | Xiao Liu
[1] Ana Maria Mendonça,et al. End-to-End Adversarial Retinal Image Synthesis , 2018, IEEE Transactions on Medical Imaging.
[2] Timo Aila,et al. A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Serge J. Belongie,et al. Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[4] Fan Zhang,et al. Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation , 2019, MICCAI.
[5] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[6] Su Ruan,et al. Medical Image Synthesis with Context-Aware Generative Adversarial Networks , 2016, MICCAI.
[7] Sotirios A. Tsaftaris,et al. Multimodal Cardiac Segmentation Using Disentangled Representation Learning , 2019, STACOM@MICCAI.
[8] Haiyong Zheng,et al. TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation , 2020, Sensors.
[9] Mohammad Havaei,et al. Conditional Generation of Medical Images via Disentangled Adversarial Inference , 2020, Medical Image Anal..
[10] Pheng-Ann Heng,et al. Unsupervised Retina Image Synthesis via Disentangled Representation Learning , 2019, SASHIMI@MICCAI.
[11] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[12] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[13] John T. Guibas,et al. Synthetic Medical Images from Dual Generative Adversarial Networks , 2017, ArXiv.
[14] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[15] Aykut Erdem,et al. Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks , 2018, IEEE Transactions on Medical Imaging.
[16] Youbao Tang,et al. CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation , 2018, MICCAI.
[17] Hao Chen,et al. Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion , 2019, MICCAI.
[18] Albert C. S. Chung,et al. Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks , 2018, BrainLes@MICCAI.
[19] Neil Smith,et al. Latent Filter Scaling for Multimodal Unsupervised Image-To-Image Translation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Taesung Park,et al. Semantic Image Synthesis With Spatially-Adaptive Normalization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Sergio Escalera,et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge , 2021, IEEE Transactions on Medical Imaging.
[22] Jeffrey L. Gunter,et al. Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks , 2018, SASHIMI@MICCAI.
[23] Yedid Hoshen,et al. Demystifying Inter-Class Disentanglement , 2020, ICLR.
[24] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[25] 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.
[26] Masoom A. Haider,et al. ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks , 2018, ArXiv.
[27] Hayit Greenspan,et al. Virtual PET Images from CT Data Using Deep Convolutional Networks: Initial Results , 2017, SASHIMI@MICCAI.
[28] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[29] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[30] T. Sørensen,et al. A method of establishing group of equal amplitude in plant sociobiology based on similarity of species content and its application to analyses of the vegetation on Danish commons , 1948 .
[31] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[32] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[34] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[35] Sotirios A. Tsaftaris,et al. Disentangled representation learning in cardiac image analysis , 2019, Medical Image Anal..
[36] Hayit Greenspan,et al. GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.
[37] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[38] Peter Wonka,et al. Disentangled Image Generation Through Structured Noise Injection , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).