PE-MVCNet: Multi-view and Cross-modal Fusion Network for Pulmonary Embolism Prediction

arXiv:2402.17187v1 Announce Type: cross
Abstract: The early detection of a pulmonary embolism (PE) is critical for enhancing patient survival rates. Both image-based and non-image-based features are of utmost importance in medical classification tasks. In a clinical setting, physicians tend to rely on the contextual information provided by Electronic Medical Records (EMR) to interpret medical imaging. However, very few models effectively integrate clinical information with imaging data. To address this shortcoming, we suggest a multimodal fusion methodology, termed PE-MVCNet, which capitalizes …

abstract arxiv classification clinical cs.cv detection eess.iv electronic features fusion image importance information medical medical records modal network patient physicians prediction records survival tasks type view