91 citations · 236 across the 31 of their papers we have counts for
11 papers · 1 filter
MEASURE: Multi-scale Minimal Sufficient Representation Learning for Domain Generalization in Sleep Staging
Sangmin Jo, Jee Seok Yoon, Wootaek Jeong +2
Deep learning-based automatic sleep staging has significantly advanced in performance and plays a crucial role in the diagnosis of sleep disorders. However, those models often stru…
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation Learning
Wonsik Jung, Eunjin Jeon, Eunsong Kang +1
Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder…
Knowledge-Induced Medicine Prescribing Network for Medication Recommendation
Ahmad Wisnu Mulyadi, Heung-Il Suk
Extensive adoption of electronic health records (EHRs) offers opportunities for their use in various downstream clinical analyses. To accomplish this purpose, enriching an EHR coho…
XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning
Ahmad Wisnu Mulyadi, Wonsik Jung, Kwanseok Oh +2
Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristic…
TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging
Jauen Phyo, Wonjun Ko, Eunjin Jeon +1
Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine learning as well as deep learning arc…
Deep Efficient Continuous Manifold Learning for Time Series Modeling
Seungwoo Jeong, Wonjun Ko, Ahmad Wisnu Mulyadi +1
Modeling non-Euclidean data is drawing extensive attention along with the unprecedented successes of deep neural networks in diverse fields. Particularly, a symmetric positive defi…
Multi-view Integration Learning for Irregularly-sampled Clinical Time Series
Yurim Lee, Eunji Jun, Heung-Il Suk
Electronic health record (EHR) data is sparse and irregular as it is recorded at irregular time intervals, and different clinical variables are measured at each observation point.…
Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction
Eunji Jun, Ahmad Wisnu Mulyadi, Jaehun Choi +1
Electronic health records (EHR) are characterized as non-stationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inher…
Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series
Ahmad Wisnu Mulyadi, Eunji Jun, Heung-Il Suk
Electronic health records (EHR) consist of longitudinal clinical observations portrayed with sparsity, irregularity, and high-dimensionality, which become major obstacles in drawin…
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCI
Eunjin Jeon, Wonjun Ko, Jee Seok Yoon +1
In recent years, deep learning-based feature representation methods have shown a promising impact in electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless,…
A Plug-in Method for Representation Factorization in Connectionist Models
Jee Seok Yoon, Myung-Cheol Roh, Heung-Il Suk
In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically control…