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.

在这项工作中，我们介绍了第一个端到端训练框架，该框架同时整合了（i）用于避免附着在假象相关性上的流行去偏置分类器（例如分布鲁棒优化（DRO））和（ii）用于揭示与任务相关的可推广成像标记的反事实图像生成。此外，我们提出了一种新的度量标准，即假象相关性附着分数（SCLS），用于量化分类器依赖假象相关性的程度，该程度由反事实图像揭示。通过对两个公共数据集进行全面实验（包括模拟和真实视觉伪迹），我们证明了去偏置方法：（i）能够学习到可推广的标记，并且（ii）成功忽略假象相关性，专注于潜在的疾病病理。