  Medical imaging cohorts are often confounded by factors such as acquisition
devices, hospital sites, patient backgrounds, and many more. As a result, deep
learning models tend to learn spurious correlations instead of causally related
features, limiting their generalizability to new and unseen data. This problem
can be addressed by minimizing dependence measures between intermediate
representations of task-related and non-task-related variables. These measures
include mutual information, distance correlation, and the performance of
adversarial classifiers. Here, we benchmark such dependence measures for the
task of preventing shortcut learning. We study a simplified setting using
Morpho-MNIST and a medical imaging task with CheXpert chest radiographs. Our
results provide insights into how to mitigate confounding factors in medical
imaging.
