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

Amandine Crombé, Masako Kataoka

2024Diagnostic and Interventional Imaging22 citationsDOI

Topics & Concepts

InterpretabilityMedicineBreast cancerArtificial intelligenceMachine learningCancerMedical physicsInternal medicineComputer scienceRadiomics and Machine Learning in Medical ImagingAI in cancer detectionGene expression and cancer classification
Breast cancer molecular subtype prediction: Improving interpretability of complex machine-learning models based on multiparametric-MRI features using SHapley Additive exPlanations (SHAP) methodology | Litcius