  The success of machine learning methods for computer vision tasks has driven
a surge in computer assisted prediction for medicine and biology. Based on a
data-driven relationship between input image and pathological classification,
these predictors deliver unprecedented accuracy. Yet, the numerous approaches
trying to explain the causality of this learned relationship have fallen short:
time constraints, coarse, diffuse and at times misleading results, caused by
the employment of heuristic techniques like Gaussian noise and blurring, have
hindered their clinical adoption.
  In this work, we discuss and overcome these obstacles by introducing a
neural-network based attribution method, applicable to any trained predictor.
Our solution identifies salient regions of an input image in a single
forward-pass by measuring the effect of local image-perturbations on a
predictor's score. We replace heuristic techniques with a strong neighborhood
conditioned inpainting approach, avoiding anatomically implausible, hence
adversarial artifacts. We evaluate on public mammography data and compare
against existing state-of-the-art methods. Furthermore, we exemplify the
approach's generalizability by demonstrating results on chest X-rays. Our
solution shows, both quantitatively and qualitatively, a significant reduction
of localization ambiguity and clearer conveying results, without sacrificing
time efficiency.
