  Fusing multi-modal data can improve the performance of deep learning models.
However, missing modalities are common for medical data due to patients'
specificity, which is detrimental to the performance of multi-modal models in
applications. Therefore, it is critical to adapt the models to missing
modalities. This study aimed to develop an efficient multi-modal fusion
architecture for medical data that was robust to missing modalities and further
improved the performance on disease diagnosis.X-ray chest radiographs for the
image modality, radiology reports for the text modality, and structured value
data for the tabular data modality were fused in this study. Each modality pair
was fused with a Transformer-based bi-modal fusion module, and the three
bi-modal fusion modules were then combined into a tri-modal fusion framework.
Additionally, multivariate loss functions were introduced into the training
process to improve model's robustness to missing modalities in the inference
process. Finally, we designed comparison and ablation experiments for
validating the effectiveness of the fusion, the robustness to missing
modalities and the enhancements from each key component. Experiments were
conducted on MIMIC-IV, MIMIC-CXR with the 14-label disease diagnosis task.
Areas under the receiver operating characteristic curve (AUROC), the area under
the precision-recall curve (AUPRC) were used to evaluate models' performance.
The experimental results demonstrated that our proposed multi-modal fusion
architecture effectively fused three modalities and showed strong robustness to
missing modalities. This method is hopeful to be scaled to more modalities to
enhance the clinical practicality of the model.
