5 papers
Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment
Sheng Liu, Long Chen, Zeyun Zhao +16
Modern clinical practice increasingly depends on reasoning over heterogeneous, evolving, and incomplete patient data. Although recent advances in multimodal foundation models have…
Multi-modal Data Spectrum: Multi-modal Datasets are Multi-dimensional
Divyam Madaan, Varshan Muhunthan, Kyunghyun Cho +1
Understanding the interplay between intra-modality dependencies (the contribution of an individual modality to a target task) and inter-modality dependencies (the relationships bet…
Temporal Generalization: A Reality Check
Divyam Madaan, Sumit Chopra, Kyunghyun Cho
Machine learning (ML) models often struggle to maintain performance under distribution shifts, leading to inaccurate predictions on unseen future data. In this work, we investigate…
Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
Divyam Madaan, Taro Makino, Sumit Chopra +1
Supervised multi-modal learning involves mapping multiple modalities to a target label. Previous studies in this field have concentrated on capturing in isolation either the inter-…
HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis
Haoxu Huang, Cem M. Deniz, Kyunghyun Cho +2
Chest X-ray imaging is a widely accessible and non-invasive diagnostic tool for detecting thoracic abnormalities. While numerous AI models assist radiologists in interpreting these…