クスダ　カオリ   KUSUDA Kaori   楠田　佳緒    所属   医学研究科 医学研究科 (医学部医学科をご参照ください)    職種   非常勤講師
論文種別	原著
言語種別	英語
査読の有無	査読なし
表題	Computerized Classification Method for Molecular Subtypes of Low-Grade Gliomas on Brain MR Images Using Modified ArcFace With Gram-Schmidt Orthogonalization
掲載誌名	正式名：IEEE Access ISSNコード：21693536
掲載区分	国内
出版社	IEEE
巻・号・頁	12,pp.194540-194550
著者・共著者	HIZUKURI Akiyoshi†, TANAKA Daiki, NAKAYAMA Ryohei , KUSUDA Kaori, MASAMUNE Ken, MURAGAKI Yoshihiro
発行年月	2024/12/19
概要	Researchers have previously developed a computerized classification method for molecular subtypes of low-grade gliomas (LGGs) on brain magnetic resonance (MR) images using three-dimensional convolutional neural networks (3D-CNN) with softmax cross-entropy loss. However, a CNN with softmax cross-entropy loss cannot obtain sufficient discrimination power because it does not explicitly enforce any margin between each class label. Thus, in this study, we propose a new multiscale 3D-attention branch network (MS3D-ABN) using modified ArcFace, a deep metric learning method, with Gram-Schmidt orthogonalization for classifying molecular subtypes of LGGs. Our database included T1- and T2-weighted brain MR images of 217 patients from Tokyo Women’s Medical University. LGGs included isocitrate dehydrogenase-wildtype astrocytomas ( n=49 ), isocitrate dehydrogenase-mutant astrocytomas ( n=58 ), and oligodendrogliomas ( n=110 ). The MS3D-ABN using modified ArcFace with Gram-Schmidt orthogonalization learned how to make samples from feature vectors of the same class labels close to each other and further separate samples from different class labels to improve discrimination power. Gram-Schmidt orthogonalization played a role in maintaining orthogonality of feature vectors for each class label. The average classification accuracy of the proposed network was 66.8% (145/217), which was an improvement compared to MS3D-ABN without ArcFace (63.6%; 138/217) and MS3D-ABN with ArcFace (65.4%; 142/217).
DOI	10.1109/ACCESS.2024.3520165