Art

J-GLOBAL ID：201902268377721942   Reference number：19A1820694

Learning More with Less: GAN-based Medical Image Augmentation

より少ない情報を用いるより多くの学習:GaNベース医用画像増強

Author (5)： HAN Changhee (Graduate School of Information Science and Technology, The University of Tokyo) ,  HAN Changhee (Research Center for Medical Bigdata, National Institute of Informatics) ,  MURAO Kohei (Research Center for Medical Bigdata, National Institute of Informatics) ,  SATOH Shin'ichi (Research Center for Medical Bigdata, National Institute of Informatics) ,  NAKAYAMA Hideki (Graduate School of Information Science and Technology, The University of Tokyo)
Material： Medical Imaging Technology (Web)  (Med Imaging Technol (Web))
Volume： 37  Issue： 3  Page： 137-142(J-STAGE)  Publication year： 2019 
JST Material Number： U0504A  ISSN： 2185-3193  Document type： Article
Article type： 原著論文  Country of issue： Japan (JPN)  Language： ENGLISH (EN)

Thesaurus term： Machine Learning ,...
Semi thesaurus term： object detection ,...

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Author keywords (3)： Generative adversarial networks ,  Data augmentation ,  Medical image augmentation

JST classification (2)： Diagnosis of tumors(=neoplasms)  (GE03020J) ,  Medical image processing  (JE04020T)

Reference (14)：

• [1] Goodfellow I, Pouget-Abadie J, Mirza M, et al.: Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS) 27, Montréal, 2014, pp. 2672-2680
• [2] Shrivastava A, Pfister T, Tuzel O, et al.: Learning from simulated and unsupervised images through adversarial training. In proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 2017, pp. 2107-2116
• [3] Han C, Rundo L, Araki R, et al.: Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection. In Neural Approaches to Dynamics of Signal Exchanges, Smart Innovation, Systems and Technologies, Springer, arXiv:1903.12571, 2019
• [4] Frid-Adar M, Diamant I, Klang E, et al.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321: 321-331, 2018
• [5] Jin D, Xu Z, Tang Y, et al.: CT-realistic lung nodule simulation from 3D conditional generative adversarial networks for robust lung segmentation. In proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Granada, 2018, pp. 732-740

Terms in the title (4)： 学習 ,  GaN ,  医用画像 ,  増強