Debiasing Counterfactuals In the Presence of Spurious Correlations
Amar Kumar Nima Fathi Raghav Mehta Brennan Nichyporuk Jean-Pierre R. Falet Sotirios Tsaftaris Tal Arbel
Deep learning models can perform well in complex medical imaging
classification tasks, even when basing their conclusions on spurious
correlations (i.e. confounders), should they be prevalent in the training
dataset, rather than on the causal image markers of interest. This would
thereby limit their ability to generalize across the population. Explainability
based on counterfactual image generation can be used to expose the confounders
but does not provide a strategy to mitigate the bias. In this work, we
introduce the first end-to-end training framework that integrates both (i)
popular debiasing classifiers (e.g. distributionally robust optimization (DRO))
to avoid latching onto the spurious correlations and (ii) counterfactual image
generation to unveil generalizable imaging markers of relevance to the task.
Additionally, we propose a novel metric, Spurious Correlation Latching Score
(SCLS), to quantify the extent of the classifier reliance on the spurious
correlation as exposed by the counterfactual images. Through comprehensive
experiments on two public datasets (with the simulated and real visual
artifacts), we demonstrate that the debiasing method: (i) learns generalizable
markers across the population, and (ii) successfully ignores spurious
correlations and focuses on the underlying disease pathology.