LAUSR

Convolutional neural networks (CNNs) are increasingly being explored and used for a variety of classification tasks in medical imaging, but current methods for post hoc explainability are limited. Most commonly used methods highlight portions of the input image that contribute to classification. While this provides a form of spatial localization relevant for focal disease processes, it may not be sufficient for co-localized or diffuse disease processes such as pulmonary edema or fibrosis. For the latter, new methods are required to isolate diffuse texture features employed by the CNN where localization alone is ambiguous. We therefore propose a novel strategy for eliciting explainability, called Feature Interpretation using Generative Adversarial Networks (FIGAN), which provides visualization of features used by a CNN for classification or regression. FIGAN uses a conditional generative adversarial network to synthesize images that span the range of a CNN’s principal embedded features. We apply FIGAN to two previously developed CNNs and show that the resulting feature interpretations can clarify ambiguities within attention areas highlighted by existing explainability methods. In addition, we perform a series of experiments to study the effect of auxiliary segmentations, training sample size, and image resolution on FIGAN’s ability to provide consistent and interpretable synthetic images.