While several previous studies have devised methods for segmentation of polyps, most of these methods are not rigorously assessed on multi-center datasets. Variability due to appearance of polyps from one center to another, difference in endoscopic instrument grades, and acquisition quality result in methods with good performance on in-distribution test data, and poor performance on out-of-distribution or underrepresented samples. Unfair models have serious implications and pose a critical challenge to clinical applications. We adapt an implicit bias mitigation method which leverages Bayesian epistemic uncertainties during training to encourage the model to focus on underrepresented sample regions. We demonstrate the potential of this approach to improve generalisability without sacrificing state-of-the-art performance on a challenging multi-center polyp segmentation dataset (PolypGen) with different centers and image modalities.

翻译：尽管已有多个研究提出了息肉分割方法，但多数方法未在多中心数据集上经过严格评估。不同中心间息肉外观的差异、内窥镜仪器等级的不同以及采集质量的差异，导致这些方法在分布内测试数据上表现良好，而在分布外或代表性不足的样本上性能较差。不公平的模型会带来严重后果，对临床应用构成严峻挑战。我们采用了一种隐式偏差缓解方法，在训练过程中利用贝叶斯认知不确定性，引导模型聚焦于代表性不足的样本区域。我们证明了该方法能够在保持最先进性能的同时，提升模型在具有不同中心和图像模态的多中心息肉分割数据集（PolypGen）上的泛化能力。