Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.

翻译：深度学习分类器容易偏向数据集中占主导地位的混淆因子，而非与目标类别相关的因果标记，从而导致泛化能力差和预测偏差。尽管通过反事实图像生成的可解释性已成功揭示这一问题，但在存在占主导地位且多样化的伪影时，实现准确可解释性的偏差缓解策略仍未得到解决。本文提出DeCoDEx框架，展示如何利用外部预训练的二值伪影检测器在推理阶段引导基于扩散的反事实图像生成器实现准确可解释性。在CheXpert数据集上，使用合成伪影和真实视觉伪影（支撑装置）的实验表明，所提方法成功生成了改变与胸腔积液相关的因果病理标记、同时保留或忽略视觉伪影的反事实图像。将DeCoDEx生成的图像增强至ERM和Group-DRO分类器，显著改善了各类别中分布外弱势群体组的分类结果。代码已开源至https://github.com/NimaFathi/DeCoDEx。