Art

J-GLOBAL ID：201802229917658872   Reference number：18A1937463

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

肝病変分類におけるCNNパフォーマンス増加のためのGaNベース合成医用画像増強【JST・京大機械翻訳】

Author (6)： Frid-Adar Maayan (Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel) ,  Diamant Idit (Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel) ,  Klang Eyal (Department of Diagnostic Imaging, The Chaim Sheba Medical Center, Tel-Hashomer, Israel) ,  Amitai Michal (Department of Diagnostic Imaging, The Chaim Sheba Medical Center, Tel-Hashomer, Israel) ,  Goldberger Jacob (Faculty of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel) ,  Greenspan Hayit (Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel)
Material： Neurocomputing
Volume： 321  Page： 321-331  Publication year： 2018 
JST Material Number： W0360A  ISSN： 0925-2312  Document type： Article
Article type： 原著論文  Country of issue： Netherlands (NLD)  Language： ENGLISH (EN)

Abstract/Point： Deep learning methods, and in ...

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Thesaurus term： annotation ,  *image ,  cyst ,  Learning ,  network ,  *picture enhancement ,  computer architecture ,  visualization ,  *medical image ,  computer vision ,  quality ,  computed tomography
Semi thesaurus term： computed tomography ,  特異性 ,  *病巣 , 【Automatic Indexing@JST】

Author keywords (7)： Image synthesis ,  Data augmentation ,  Convolutional neural networks ,  Generative adversarial network ,  Deep learning ,  Liver lesions ,  Lesion classification

JST classification (3)： Artificial intelligence  (JE08000Z) ,  Graphic and image processing in general  (JE04010I) ,  Medical image processing  (JE04020T)

Terms in the title (7)： 病変 ,  分類 ,  CNN ,  パフォーマンス ,  GaN ,  医用画像 ,  増強