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