Real World Relevance of Generative Counterfactual Explanations

Abstract

The interpretability of deep learning based algorithms is critical in settings where the algorithm must provide actionable information such as clinical diagnoses or instructions in autonomous driving. Image based explanations or feature attributions are an often-proposed solution for natural imaging datasets, but their utility for mission critical settings is unclear. In this work, we provide image explanations that are both semantically interpretable and assess their utility for real world relevance using imaging data extracted from clinical settings. We address the problem of pneumonia classification from Chest X-ray images where we show that (1) by perturbing specific latent dimensions of a GAN based model, the classifier predictions can be flipped and (2) the latent factors have clinical relevance. We demonstrate the latter by performing a case study with a board-certified radiologist and identify some latent factors that are clinically informative and others that may capture spurious correlations.