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

IF 4.9 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Diagnostic and Interventional Imaging Pub Date : 2024-05-01 DOI:10.1016/j.diii.2024.01.008
Amandine Crombé, Masako Kataoka
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乳腺癌分子亚型预测:使用 SHapley Additive exPlanations (SHAP) 方法提高基于多参数磁共振成像特征的复杂机器学习模型的可解释性。
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来源期刊
Diagnostic and Interventional Imaging
Diagnostic and Interventional Imaging Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
8.50
自引率
29.10%
发文量
126
审稿时长
11 days
期刊介绍: Diagnostic and Interventional Imaging accepts publications originating from any part of the world based only on their scientific merit. The Journal focuses on illustrated articles with great iconographic topics and aims at aiding sharpening clinical decision-making skills as well as following high research topics. All articles are published in English. Diagnostic and Interventional Imaging publishes editorials, technical notes, letters, original and review articles on abdominal, breast, cancer, cardiac, emergency, forensic medicine, head and neck, musculoskeletal, gastrointestinal, genitourinary, interventional, obstetric, pediatric, thoracic and vascular imaging, neuroradiology, nuclear medicine, as well as contrast material, computer developments, health policies and practice, and medical physics relevant to imaging.
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