CNNを用いたGliomaの疾患進行度評価における形状特徴量に関する一検討

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2020/12/01

タイトル(英語): A Study on Nuclei Shape Features at the Classification of Glioma Disease Stage Using CNN

著者名: 齊藤　大祐（三重大学大学院工学研究科電気電子専攻 情報処理研究室），川中　普晴（三重大学大学院工学研究科電気電子専攻 情報処理研究室），V. B. Surya　Prasath（Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center），Bruce J.　Aronow（Division of Biomedical Informatics, Cincinnati Children's Hospital M

著者名(英語): Daisuke Saito (Division of Electrical and Electronic Engineering, Graduate School of Engineering, Mie University), Hiroharu Kawanaka (Division of Electrical and Electronic Engineering, Graduate School of Engineering, Mie University), V. B. Surya Prasath (Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center), Bruce J. Aronow (Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center)

キーワード: バイオメディカルインフォマティクス，Glioma，CNN　　Biomedical Informatics，Glioma，CNN

要約(英語): Recently, a lot of studies using Deep Learning techniques have been reported in the field of Digital Histopathology. For instance, there are ideas using deep Convolutional Neural Network (CNN) for disease stage classification and segmentation. These methods are expected to reduce pathologists’ work and realize quantitative analysis. However, at the disease stage classification using CNN, even if we can obtain high classification accuracy, it is difficult for us to understand how CNN decides the disease stage. In this paper, we discussed the relationship between features of cell nuclei shape and the disease stage classification using CNN.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.140 No.12 （2020） 特集：電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1367-1368 p

原稿種別: 研究開発レター／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/140/12/140_1367/_article/-char/ja/