Yehong Huang

UMCA-Net: Uncertainty-aware multi-stage cross-attention for cost-aware multi-omics data classification

This work proposes an uncertainty-aware multi-view dynamic decision framework for efficient and trustworthy disease classification that provides a scalable and practical solution for balancing diagnostic accuracy and economic cost, facilitating the deployment of multi-omics models in real-world clinical settings.