
This paper formalizes the goals and elements of interpretability in machine learning for medical imaging (MLMI) from an applied perspective, in order to guide method design and improve real-world usage.


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a-framework-for-interpretability-in-machine-learning-for-medical-imaging-formalizing-goals-and-elements-to-guide-practical-application


A Framework for Interpretability in Machine Learning for Medical Imaging: Formalizing Goals and Elements to Guide Practical Application
