Shortcut learning is when a model -- e.g. a cardiac disease classifier --
exploits correlations between the target label and a spurious shortcut feature,
e.g. a pacemaker, to predict the target label based on the shortcut rather than
real discriminative features. This is common in medical imaging, where
treatment and clinical annotations correlate with disease labels, making them
easy shortcuts to predict disease. We propose a novel detection and
quantification of the impact of potential shortcut features via a fast
diffusion-based counterfactual image generation that can synthetically remove
or add shortcuts. Via a novel inpainting-based modification we spatially limit
the changes made with no extra inference step, encouraging the removal of
spatially constrained shortcut features while ensuring that the shortcut-free
counterfactuals preserve their remaining image features to a high degree. Using
these, we assess how shortcut features influence model predictions.
This is enabled by our second contribution: An efficient diffusion-based
counterfactual explanation method with significant inference speed-up at
comparable image quality as state-of-the-art. We confirm this on two large
chest X-ray datasets, a skin lesion dataset, and CelebA.

通过快速扩散基于反事实的图像生成和基于修补的修改方法，我们检测和量化潜在的捷径特征对模型预测的影响。我们通过在两个大型胸部 X 射线数据集、一个皮肤病损数据集和 CelebA 上确认，验证了我们的方法在推理速度显著提升、图像质量与最先进技术可比的同时的有效性。