Abstract
Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer
vision, with applications ranging from weakly supervised
localisation to understanding hidden effects in the data.
In recent years, approaches based on interpreting a previously trained neural network classifier have become the
de facto state-of-the-art and are commonly used on medical as well as natural image datasets. In this paper, we
discuss a limitation of these approaches which may lead
to only a subset of the category specific features being detected. To address this problem we develop a novel feature attribution technique based on Wasserstein Generative
Adversarial Networks (WGAN), which does not suffer from
this limitation. We show that our proposed method performs
substantially better than the state-of-the-art for visual attribution on a synthetic dataset and on real 3D neuroimaging
data from patients with mild cognitive impairment (MCI)
and Alzheimer’s disease (AD). For AD patients the method
produces compellingly realistic disease effect maps which
are very close to the observed effects.