イクノ　ソウイチロウ   Soichiro Ikuno   生野　壮一郎    所属   コンピュータサイエンス学部 コンピュータサイエンス学科    職種   教授
言語種別	英語
発行・発表の年月	2025/04
形態種別	学術論文
査読	査読あり
標題	Analysis of a 3D brain MRI sex classifier via Approximate Inverse Model Explanations
執筆形態	共著
掲載誌名	JASSE
掲載区分	国内
出版社・発行元	日本シミュレーション学会英文誌
巻・号・頁	13(1)
著者・共著者	Kosuke Yano, Takafumi Nakanishi, Soichiro Ikuno
概要	In this study, we have analyzed the prediction rationale of a deep learning model for sex classification from 3D brain MRI using Approximate Inverse Model Explanations (AIME). A 3D DenseNet121 classifier has been trained on 566 T1-weighted IXI scans. The model has achieved 98.2% accuracy on a 114-case validation set. Global importance has shown a sign-reversal pattern between classes: peripheral regions contribute to Male prediction, whereas central regions contribute to Female prediction. Local importance has been consistent with this pattern and has highlighted strong peripheral reliance in misclassified cases. Controlled experiments (skull-stripped retraining, masking sensitivity, and age-matched analysis) have indicated substantial dependence on extra-brain information. Cross-validation and leave-one-site-out evaluation have supported the robustness of these findings.
DOI	https://doi.org/10.15748/jasse.13.44
外部リンクURL	https://www.jstage.jst.go.jp/article/jasse/13/1/13_44/_article/-char/ja/