Deep learning could be prone to learning shortcuts raised by dataset bias and result in inaccurate, unreliable, and unfair models, which impedes its adoption in real-world clinical applications. Despite its significance, there is a dearth of research in the medical image classification domain to address dataset bias. Furthermore, the bias labels are often agnostic, as identifying biases can be laborious and depend on post-hoc interpretation. This paper proposes learning Adaptive Agreement from a Biased Council (Ada-ABC), a debiasing framework that does not rely on explicit bias labels to tackle dataset bias in medical images. Ada-ABC develops a biased council consisting of multiple classifiers optimized with generalized cross entropy loss to learn the dataset bias. A debiasing model is then simultaneously trained under the guidance of the biased council. Specifically, the debiasing model is required to learn adaptive agreement with the biased council by agreeing on the correctly predicted samples and disagreeing on the wrongly predicted samples by the biased council. In this way, the debiasing model could learn the target attribute on the samples without spurious correlations while also avoiding ignoring the rich information in samples with spurious correlations. We theoretically demonstrated that the debiasing model could learn the target features when the biased model successfully captures dataset bias. Moreover, to our best knowledge, we constructed the first medical debiasing benchmark from four datasets containing seven different bias scenarios. Our extensive experiments practically showed that our proposed Ada-ABC outperformed competitive approaches, verifying its effectiveness in mitigating dataset bias for medical image classification. The codes and organized benchmark datasets will be made publicly available.

翻译：深度学习易受数据集偏见引发的快捷学习影响，导致模型不准确、不可靠且不公平，这阻碍了其在真实临床场景中的应用。尽管该问题至关重要，但医学图像分类领域中针对数据集偏见的研究仍显不足。此外，偏见标签往往难以获取，因为识别偏见过程繁琐且依赖事后解释。本文提出通过学习有偏委员会的自适应一致性（Ada-ABC），这是一种无需显式偏见标签即可处理医学图像数据偏见的去偏框架。Ada-ABC构建了一个由多个分类器组成的有偏委员会，这些分类器通过广义交叉熵损失进行优化以学习数据集偏见。在委员会指导下，同步训练一个去偏模型：具体而言，去偏模型需学习与委员会的自适应一致性，即对委员会正确预测的样本保持赞同，对委员会错误预测的样本提出异议。通过这种方式，去偏模型能在无虚假相关性的样本上学习目标属性，同时避免忽略含有虚假相关性样本中的丰富信息。我们从理论上证明，当有偏模型成功捕获数据集偏见时，去偏模型能够学习目标特征。此外，据我们所知，本研究首次构建了包含四个数据集、七种不同偏见场景的医学去偏基准测试。大量实验表明，我们提出的Ada-ABC方法优于现有竞争方法，验证了其在缓解医学图像分类数据集偏见方面的有效性。相关代码与整理的基准数据集将公开提供。