5 papers · 1 filter
Curriculum-Aware Interpolate-then-Refine: Learned Physiological Time-Series Imputation under Realistic Missingness
Yu-Chao Huang, Haochen Zhang, Nicholas Konz +1
Imputing physiological time series (arterial blood pressure, blood glucose, etc.) is essential for addressing the missingness that pervades clinical data. Yet modern imputation met…
Represent, Then Generate: Multimodal-Conditioned Time-Series Generation under Irregular Missingness
Haochen Zhang, Jiaheng Guo, Yu-Chao Huang +2
Continuous physiological time series underpin modern clinical monitoring, yet many of the most informative signals are invasive, expensive, or simply unavailable for a given patien…
PAMF: Prior-Aware Multimodal Fusion for Incomplete Time Series Data
Ziwen Kan, Wugeng Zheng, Tianlong Chen +1
In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire resp…
MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion
Wugeng Zheng, Ziwen Kan, Tianlong Chen +2
Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two failure modes are common: modali…
Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
Farica Zhuang, Shu Yang, Dinara Aliyeva +6
Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data…