  There is a heated debate on how to interpret the decisions provided by deep
learning models (DLM), where the main approaches rely on the visualization of
salient regions to interpret the DLM classification process. However, these
approaches generally fail to satisfy three conditions for the problem of lesion
detection from medical images: 1) for images with lesions, all salient regions
should represent lesions, 2) for images containing no lesions, no salient
region should be produced,and 3) lesions are generally small with relatively
smooth borders. We propose a new model-agnostic paradigm to interpret DLM
classification decisions supported by a novel definition of saliency that
incorporates the conditions above. Our model-agnostic 1-class saliency detector
(MASD) is tested on weakly supervised breast lesion detection from DCE-MRI,
achieving state-of-the-art detection accuracy when compared to current
visualization methods.
