A joint sequence-image framework with contrastive and clustering losses improves classification of irregular medical time series with heavy missingness.
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Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning
A joint sequence-image framework with contrastive and clustering losses improves classification of irregular medical time series with heavy missingness.