A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

Abstract

A considerable portion of computer aided diagnosis studies provide a form of explainability of their deep learning models for the purpose of model visualization and inspection. The techniques commonly chosen by these studies (class activation mapping, feature activation mapping and t-distributed stochastic neighbor embedding) have potential limitations. Because researchers generally do not measure the quality of their explanations, we are agnostic about how effective these explanations are at addressing the black box issues of deep learning in radiology.

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