Deep learning models have shown great promise in various healthcare monitoring applications. However, most healthcare datasets with high-quality (gold-standard) labels are small-scale, as directly collecting ground truth is often costly and time-consuming. As a result, models developed and validated on small-scale datasets often suffer from overfitting and do not generalize well to unseen scenarios. At the same time, large amounts of imprecise (silver-standard) labeled data, annotated by approximate methods with the help of modern wearables and in the absence of ground truth validation, are starting to emerge. However, due to measurement differences, this data displays significant label distribution shifts, which motivates the use of domain adaptation. To this end, we introduce UDAMA, a method with two key components: Unsupervised Domain Adaptation and Multidiscriminator Adversarial Training, where we pre-train on the silver-standard data and employ adversarial adaptation with the gold-standard data along with two domain discriminators. In particular, we showcase the practical potential of UDAMA by applying it to Cardio-respiratory fitness (CRF) prediction. CRF is a crucial determinant of metabolic disease and mortality, and it presents labels with various levels of noise (goldand silver-standard), making it challenging to establish an accurate prediction model. Our results show promising performance by alleviating distribution shifts in various label shift settings. Additionally, by using data from two free-living cohort studies (Fenland and BBVS), we show that UDAMA consistently outperforms up to 12% compared to competitive transfer learning and state-of-the-art domain adaptation models, paving the way for leveraging noisy labeled data to improve fitness estimation at scale.

翻译：深度学习模型在多种医疗健康监测应用中展现出巨大潜力。然而，大多数具有高质量（金标准）标签的医疗数据集规模较小，因为直接获取真实标签通常成本高昂且耗时。因此，基于小规模数据集开发并验证的模型容易过拟合，且难以泛化至未见场景。与此同时，通过近似方法借助现代可穿戴设备在缺乏真实标签验证的情况下标注的大量不精确（银标准）标签数据正开始涌现。但由于测量差异，这些数据呈现出显著的标签分布偏移，这促使我们采用域适应技术。为此，我们提出UDAMA方法，包含两个关键组件：无监督域适应与多判别器对抗训练——我们预先在银标准数据上训练模型，然后利用金标准数据与两个域判别器进行对抗适应。具体而言，我们通过将其应用于心肺适能预测，展示了UDAMA的实践潜力。心肺适能是代谢疾病与死亡率的关键决定因素，其标签包含不同噪声水平（金标准与银标准），这使得建立准确预测模型具有挑战性。实验结果表明，该方法能有效缓解多种标签偏移场景下的分布偏移问题。此外，基于两项自由生活队列研究（Fenland与BBVS）的数据，我们发现UDAMA相比竞争性迁移学习与最先进的域适应模型，性能持续提升高达12%，这为利用噪声标签数据大规模改进健身评估铺平了道路。