  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 on Computed Tomography Pulmonary Angiography imaging and EMR
data. This method comprises the Image-only module with an integrated multi-view
block, the EMR-only module, and the Cross-modal Attention Fusion (CMAF) module.
These modules cooperate to extract comprehensive features that subsequently
generate predictions for PE. We conducted experiments using the publicly
accessible Stanford University Medical Center dataset, achieving an AUROC of
94.1%, an accuracy rate of 90.2%, and an F1 score of 90.6%. Our proposed model
outperforms existing methodologies, corroborating that our multimodal fusion
model excels compared to models that use a single data modality. Our source
code is available at https://github.com/LeavingStarW/PE-MVCNET.
