Breast cancer molecular subtype prediction: Improving interpretability of complex machine-learning models based on multiparametric-MRI features using SHapley Additive exPlanations (SHAP) methodology

被引：2

作者：

Crombe, Amandine ^{[1
,2
]}

Kataoka, Masako ^{[3
]}

机构：

[1] Pellegrin Univ Hosp, Dept Radiol, F-33000 Bordeaux, France

[2] Bordeaux Inst Oncol BRIC, SARCOTARGET Team, INSERM, U1312, F-33076 Bordeaux, France

[3] Kyoto Univ, Grad Sch Med, Dept Diagnost Imaging & Nucl Med, Kyoto 6068507, Japan

来源：

DIAGNOSTIC AND INTERVENTIONAL IMAGING | 2024年 / 105卷 / 05期

关键词：

Breast cancer; Decision tree; Ensemble trees; Explainable artificial intelligence; SHapley Additive exPlanations (SHAP);

D O I：

10.1016/j.diii.2024.01.008

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

引用

页码：161 / 162

页数：2

共 14 条

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