UDAMA: Unsupervised Domain Adaptation through Multi-discriminator
  Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction
Yu Wu Dimitris Spathis Hong Jia Ignacio Perez-Pozuelo Tomas Gonzales Soren Brage Nicholas Wareham Cecilia Mascolo
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.