Multi-View Contrastive Learning for Robust Domain Adaptation in Medical Time Series Analysis
This work addresses the problem of robust domain adaptation in medical time series for healthcare AI deployment, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackled the challenge of adapting machine learning models to medical time series across domains by proposing a multi-view contrastive learning framework that integrates temporal, derivative, and frequency features, resulting in significant outperformance over state-of-the-art methods in transfer learning tasks on EEG, ECG, and EMG datasets.
Adapting machine learning models to medical time series across different domains remains a challenge due to complex temporal dependencies and dynamic distribution shifts. Current approaches often focus on isolated feature representations, limiting their ability to fully capture the intricate temporal dynamics necessary for robust domain adaptation. In this work, we propose a novel framework leveraging multi-view contrastive learning to integrate temporal patterns, derivative-based dynamics, and frequency-domain features. Our method employs independent encoders and a hierarchical fusion mechanism to learn feature-invariant representations that are transferable across domains while preserving temporal coherence. Extensive experiments on diverse medical datasets, including electroencephalogram (EEG), electrocardiogram (ECG), and electromyography (EMG) demonstrate that our approach significantly outperforms state-of-the-art methods in transfer learning tasks. By advancing the robustness and generalizability of machine learning models, our framework offers a practical pathway for deploying reliable AI systems in diverse healthcare settings.