Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations. (arXiv:2204.00617v1 [eess.IV])

Deep learning has in recent years achieved immense success in all areas of
computer vision and has the potential of assisting medical doctors in analyzing
visual content for disease and other abnormalities. However, the current state
of deep learning is very much a black box, making medical professionals highly
skeptical about integrating these methods into clinical practice. Several
methods have been proposed in order to shine some light onto these black boxes,
but there is no consensus on the opinion of the medical doctors that will
consume these explanations. This paper presents a study asking medical doctors
about their opinion of current state-of-the-art explainable artificial
intelligence methods when applied to a gastrointestinal disease detection use
case. We compare two different categories of explanation methods, intrinsic and
extrinsic, and gauge their opinion of the current value of these explanations.
The results indicate that intrinsic explanations are preferred and that



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