  Deep neural networks (DNNs) show promise in image-based medical diagnosis,
but cannot be fully trusted since their performance can be severely degraded by
dataset shifts to which human perception remains invariant. If we can better
understand the differences between human and machine perception, we can
potentially characterize and mitigate this effect. We therefore propose a
framework for comparing human and machine perception in medical diagnosis. The
two are compared with respect to their sensitivity to the removal of clinically
meaningful information, and to the regions of an image deemed most suspicious.
Drawing inspiration from the natural image domain, we frame both comparisons in
terms of perturbation robustness. The novelty of our framework is that separate
analyses are performed for subgroups with clinically meaningful differences. We
argue that this is necessary in order to avert Simpson's paradox and draw
correct conclusions. We demonstrate our framework with a case study in breast
cancer screening, and reveal significant differences between radiologists and
DNNs. We compare the two with respect to their robustness to Gaussian low-pass
filtering, performing a subgroup analysis on microcalcifications and soft
tissue lesions. For microcalcifications, DNNs use a separate set of high
frequency components than radiologists, some of which lie outside the image
regions considered most suspicious by radiologists. These features run the risk
of being spurious, but if not, could represent potential new biomarkers. For
soft tissue lesions, the divergence between radiologists and DNNs is even
starker, with DNNs relying heavily on spurious high frequency components
ignored by radiologists. Importantly, this deviation in soft tissue lesions was
only observable through subgroup analysis, which highlights the importance of
incorporating medical domain knowledge into our comparison framework.
