2025
CLHi-MTS: A Contrastive Learning-Based Hierarchical Framework for Masked Medical Time-Series Modeling
ICASSP 2025accepted
Medical time-series analysis is crucial for the diagnosis and treatment of various diseases. As modern medical sensors evolve, the complexity and dimensionality of medical signals have increased, posing challenges for data labeling and classification. Self-supervised learning has emerged as a promis…