Selective synthetic augmentation with HistoGAN for improved histopathology image classification
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Sameer Antani | Yuan Xue | Xiaolei Huang | Carl Cornwell | Zhiyun Xue | L Rodney Long | Jiarong Ye | Qianying Zhou | Richard Zaino | Keith C Cheng | Xiaolei Huang | Yuan Xue | R. Zaino | K. Cheng | Z. Xue | Sameer Kiran Antani | Qianying Zhou | Jiarong Ye | L. R. Long | Carl Cornwell
[1] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[2] Christopher Ré,et al. Learning to Compose Domain-Specific Transformations for Data Augmentation , 2017, NIPS.
[3] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[4] Anant Gupta,et al. Generative Image Translation for Data Augmentation of Bone Lesion Pathology , 2018, MIDL.
[5] Sameer Antani,et al. Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification , 2019, MICCAI.
[6] Bogdan Raducanu,et al. Transferring GANs: generating images from limited data , 2018, ECCV.
[7] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[9] Ion Stoica,et al. Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules , 2019, ICML.
[10] Sotirios A. Tsaftaris,et al. Medical Image Computing and Computer Assisted Intervention , 2017 .
[11] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[12] Amos J. Storkey,et al. Data Augmentation Generative Adversarial Networks , 2017, ICLR 2018.
[13] Luis Perez,et al. The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.
[14] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[15] Tanveer F. Syeda-Mahmood,et al. Chest x-ray generation and data augmentation for cardiovascular abnormality classification , 2018, Medical Imaging.
[16] Ali Borji,et al. Pros and Cons of GAN Evaluation Measures , 2018, Comput. Vis. Image Underst..
[17] Marcus B. Perry,et al. The Exponentially Weighted Moving Average , 2010 .
[18] A. Madabhushi,et al. Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.
[19] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[20] Hayit Greenspan,et al. GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.
[21] Constantine Bekas,et al. BAGAN: Data Augmentation with Balancing GAN , 2018, ArXiv.
[22] Xiaogang Wang,et al. StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] R. Joe Stanley,et al. Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis With Fusion-Based Classification , 2016, IEEE Journal of Biomedical and Health Informatics.
[24] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[25] Huiqi Li,et al. Synthesizing retinal and neuronal images with generative adversarial nets , 2018, Medical Image Anal..
[26] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[27] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[28] H. Irshad,et al. Methods for Nuclei Detection, Segmentation, and Classification in Digital Histopathology: A Review—Current Status and Future Potential , 2014, IEEE Reviews in Biomedical Engineering.
[29] Xiaohua Zhai,et al. High-Fidelity Image Generation With Fewer Labels , 2019, ICML.
[30] Jean-Philippe Thiran,et al. Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network , 2018, MICCAI.
[31] Tao Xu,et al. SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation , 2017, Neuroinformatics.
[32] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[33] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[34] Max Welling,et al. Rotation Equivariant CNNs for Digital Pathology , 2018, MICCAI.
[35] Frédo Durand,et al. Data augmentation using learned transforms for one-shot medical image segmentation , 2019, ArXiv.
[36] Zhipeng Jia,et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features , 2017, BMC Bioinformatics.
[37] Joel H. Saltz,et al. Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[39] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] J. Stuart Hunter,et al. The exponentially weighted moving average , 1986 .
[41] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[42] Hugo Larochelle,et al. Modulating early visual processing by language , 2017, NIPS.
[43] Sansanee Auephanwiriyakul,et al. Automatic cervical cell segmentation and classification in Pap smears , 2014, Comput. Methods Programs Biomed..
[44] Quoc V. Le,et al. AutoAugment: Learning Augmentation Strategies From Data , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Han Zhang,et al. Self-Attention Generative Adversarial Networks , 2018, ICML.
[46] Yunchao Wei,et al. Perceptual Generative Adversarial Networks for Small Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Xin Liu,et al. Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology , 2019, Engineering.
[48] Liang Chen,et al. GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks , 2018, ArXiv.
[49] Tatsuya Harada,et al. Image Generation From Small Datasets via Batch Statistics Adaptation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[50] Saeed Hassanpour,et al. Finding a Needle in the Haystack: Attention-Based Classification of High Resolution Microscopy Images , 2018, ArXiv.
[51] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[52] Zoubin Ghahramani,et al. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.