A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang,
Batuhan K. Karaman,
Heejong Kim
et al.
Abstract:Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis… Show more
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