PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay
  for Smart Healthcare
Chia-Hao Li Niraj K. Jha
We propose PAGE, a domain-incremental adaptation strategy with past-agnostic
generative replay for smart healthcare. PAGE enables generative replay without
the aid of any preserved data or information from prior domains. When adapting
to a new domain, it exploits real data from the new distribution and the
current model to generate synthetic data that retain the learned knowledge of
previous domains. By replaying the synthetic data with the new real data during
training, PAGE achieves a good balance between domain adaptation and knowledge
retention. In addition, we incorporate an extended inductive conformal
prediction (EICP) method into PAGE to produce a confidence score and a
credibility value for each detection result. This makes the predictions
interpretable and provides statistical guarantees for disease detection in
smart healthcare applications. We demonstrate PAGE's effectiveness in
domain-incremental disease detection with three distinct disease datasets
collected from commercially available WMSs. PAGE achieves highly competitive
performance against state-of-the-art with superior scalability, data privacy,
and feasibility. Furthermore, PAGE can enable up to 75% reduction in clinical
workload with the help of EICP.