Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network

[1]  Namkug Kim,et al.  Research Highlight: Use of Generative Images Created with Artificial Intelligence for Brain Tumor Imaging , 2022, Korean journal of radiology.

[2]  Namkug Kim,et al.  Realistic high-resolution lateral cephalometric radiography generated by progressive growing generative adversarial network and quality evaluations , 2021, Scientific Reports.

[3]  Namkug Kim,et al.  Realistic High-Resolution Body Computed Tomography Image Synthesis by Using Progressive Growing Generative Adversarial Network: Visual Turing Test , 2021, JMIR medical informatics.

[4]  J. Vencovský,et al.  Plasma Hsp90 levels in patients with systemic sclerosis and relation to lung and skin involvement: a cross-sectional and longitudinal study , 2021, Scientific Reports.

[5]  K. Nozaki,et al.  Intraoral image generation by progressive growing of generative adversarial network and evaluation of generated image quality by dentists , 2020, Scientific Reports.

[6]  Abhishek Kumar,et al.  Score-Based Generative Modeling through Stochastic Differential Equations , 2020, ICLR.

[7]  Ibrahim Saad Ali,et al.  Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network , 2019, Expert Syst. Appl..

[8]  Hye-Jeong Kwon,et al.  PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity , 2021, IEEE Access.

[9]  Ian Goodfellow,et al.  Generative adversarial networks , 2020, Commun. ACM.

[10]  Ghina Berjawi,et al.  MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education , 2020, ArXiv.

[11]  Saleh Albahli,et al.  Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia , 2020, International journal of medical sciences.

[12]  Namkug Kim,et al.  Deep Learning in Medical Imaging , 2020, Neurospine.

[13]  Tero Karras,et al.  Training Generative Adversarial Networks with Limited Data , 2020, NeurIPS.

[14]  M. Mazurowski,et al.  Medical Imaging 2020: Computer-Aided Diagnosis , 2020 .

[15]  Alessandro Bria,et al.  Addressing class imbalance in deep learning for small lesion detection on medical images , 2020, Comput. Biol. Medicine.

[16]  Kazuyoshi Imaizumi,et al.  Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks , 2020, PloS one.

[17]  Fadi Thabtah,et al.  Data imbalance in classification: Experimental evaluation , 2020, Inf. Sci..

[18]  Hayit Greenspan,et al.  An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection , 2018, IEEE Journal of Biomedical and Health Informatics.

[19]  Joon Beom Seo,et al.  A Curriculum Learning Strategy to Enhance the Accuracy of Classification of Various Lesions in Chest-PA X-ray Screening for Pulmonary Abnormalities , 2019, Scientific Reports.

[20]  Ke Yan,et al.  Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks , 2019, Scientific Reports.

[21]  Shuxu Guo,et al.  Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network. , 2019, Medical physics.

[22]  Yang Lei,et al.  MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. , 2019, Medical physics.

[23]  Shahrokh Valaee,et al.  Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.

[24]  Andy Kitchen,et al.  Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning. , 2019, Radiology.

[25]  Joseph O. Deasy,et al.  Cross-modality (CT-MRI) prior augmented deep learning for robust lung tumor segmentation from small MR datasets , 2019, Medical physics.

[26]  H. Greenspan,et al.  Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide. , 2019, Radiology.

[27]  Aldenor G. Santos,et al.  Occurrence of the potent mutagens 2- nitrobenzanthrone and 3-nitrobenzanthrone in fine airborne particles , 2019, Scientific Reports.

[28]  Jong Chul Ye,et al.  Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography , 2018, Medical physics.

[29]  Hayit Greenspan,et al.  Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection , 2018, Eng. Appl. Artif. Intell..

[30]  Fujio Toriumi,et al.  Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images , 2018, Tomography.

[31]  Fei Wang,et al.  Deep learning for healthcare: review, opportunities and challenges , 2018, Briefings Bioinform..

[32]  Ge Wang,et al.  Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising , 2018, IEEE Access.

[33]  Hayit Greenspan,et al.  GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.

[34]  Jaakko Lehtinen,et al.  Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.

[35]  Atsuto Maki,et al.  A systematic study of the class imbalance problem in convolutional neural networks , 2017, Neural Networks.

[36]  Lipo Wang,et al.  Deep Learning Applications in Medical Image Analysis , 2018, IEEE Access.

[37]  C. Pal,et al.  Deep Learning: A Primer for Radiologists. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.

[38]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[39]  Jefersson Alex dos Santos,et al.  Towards better exploiting convolutional neural networks for remote sensing scene classification , 2016, Pattern Recognit..

[40]  M. McHugh.  Interrater reliability: the kappa statistic , 2012, Biochemia medica.

[41]  G. M. Allan,et al.  Kappa statistic , 2005, Canadian Medical Association Journal.

[42]  Stan Lipovetsky,et al.  Generalized Latent Variable Modeling: Multilevel,Longitudinal, and Structural Equation Models , 2005, Technometrics.

[43]  Ravishankar K. Iyer,et al.  Experimental evaluation , 1995 .