  Deep learning models are routinely employed in computational pathology
(CPath) for solving problems of diagnostic and prognostic significance.
Typically, the generalization performance of CPath models is analyzed using
evaluation protocols such as cross-validation and testing on multi-centric
cohorts. However, to ensure that such CPath solutions are robust and safe for
use in a clinical setting, a critical analysis of their predictive performance
and vulnerability to adversarial attacks is required, which is the focus of
this paper. Specifically, we show that a highly accurate model for
classification of tumour patches in pathology images (AUC > 0.95) can easily be
attacked with minimal perturbations which are imperceptible to lay humans and
trained pathologists alike. Our analytical results show that it is possible to
generate single-instance white-box attacks on specific input images with high
success rate and low perturbation energy. Furthermore, we have also generated a
single universal perturbation matrix using the training dataset only which,
when added to unseen test images, results in forcing the trained neural network
to flip its prediction labels with high confidence at a success rate of > 84%.
We systematically analyze the relationship between perturbation energy of an
adversarial attack, its impact on morphological constructs of clinical
significance, their perceptibility by a trained pathologist and saliency maps
obtained using deep learning models. Based on our analysis, we strongly
recommend that computational pathology models be critically analyzed using the
proposed adversarial validation strategy prior to clinical adoption.
