6 papers
A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detection
Miseon Park, Kijung Yoon
Time series anomaly detection is essential for the reliable operation of complex systems, but most existing methods require extensive task-specific training. We explore whether tim…
A Multimodal Approach to Alzheimer's Diagnosis: Geometric Insights from Cube Copying and Cognitive Assessments
Jaeho Yang, Kijung Yoon
Early and accessible detection of Alzheimer's disease (AD) remains a critical clinical challenge, and cube-copying tasks offer a simple yet informative assessment of visuospatial f…
Self-Supervised Discovery of Neural Circuits in Spatially Patterned Neural Responses with Graph Neural Networks
Kijung Yoon
Inferring synaptic connectivity from neural population activity is a fundamental challenge in computational neuroscience, complicated by partial observability and mismatches betwee…
Osteoporosis Prediction from Hand X-ray Images Using Segmentation-for-Classification and Self-Supervised Learning
Ung Hwang, Chang-Hun Lee, Kijung Yoon
Osteoporosis is a widespread and chronic metabolic bone disease that often remains undiagnosed and untreated due to limited access to bone mineral density (BMD) tests like Dual-ene…
Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting
Juhyeon Kim, Hyungeun Lee, Seungwon Yu +4
Multivariate time series is prevalent in many scientific and industrial domains. Modeling multivariate signals is challenging due to their long-range temporal dependencies and intr…
Osteoporosis Prediction from Hand and Wrist X-rays using Image Segmentation and Self-Supervised Learning
Hyungeun Lee, Ung Hwang, Seungwon Yu +2
Osteoporosis is a widespread and chronic metabolic bone disease that often remains undiagnosed and untreated due to limited access to bone mineral density (BMD) tests like Dual-ene…