A Framework for Interpretability in Machine Learning for Medical Imaging

arXiv:2310.01685v3 Announce Type: replace
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 …

abstract arxiv cs.lg framework general however imaging interpretability machine machine learning machine learning models medical medical imaging research sense type