基于 UK Biobank 的视网膜图像分类模型中的偏差研究
On Biases in a UK Biobank-based Retinal Image Classification Model
AI总结：
本文在 UK Biobank 视网膜图像上训练疾病分类模型，发现跨人群及评估中心的性能差异，并表明现有偏差缓解方法效果有限，需按偏差类型定制方案。
摘要：
近期研究揭示了机器学习模型在医疗保健领域表现中令人担忧的差异。在本研究中，我们通过在 UK Biobank 眼底视网膜图像上训练并评估一个疾病分类模型，探究此类差异是否存在于这些图像中。我们评估了不同人群群体之间可能存在的差异，发现尽管模型整体表现强劲，但仍存在显著差异。特别地，我们发现某些评估中心存在不公平的表现；鉴于严格的数据标准化协议，这一结果令人意外。我们比较了这些差异如何产生，并将一系列现有的偏差缓解方法应用于每一种差异。一个关键洞见是，每种差异都具有独特属性，并且对缓解方法的反应不同。我们还发现，这些方法在很大程度上无法提升公平性，这凸显出需要针对特定偏差类型定制更好的偏差缓解方法。
英文摘要：
Recent work has uncovered alarming disparities in the performance of machine learning models in healthcare. In this study, we explore whether such disparities are present in the UK Biobank fundus retinal images by training and evaluating a disease classification model on these images. We assess possible disparities across various population groups and find substantial differences despite strong overall performance of the model. In particular, we discover unfair performance for certain assessment centres, which is surprising given the rigorous data standardisation protocol. We compare how these differences emerge and apply a range of existing bias mitigation methods to each one. A key insight is that each disparity has unique properties and responds differently to the mitigation methods. We also find that these methods are largely unable to enhance fairness, highlighting the need for better bias mitigation methods tailored to the specific type of bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
