  Colorectal cancer (CRC) micro-satellite instability (MSI) prediction on
histopathology images is a challenging weakly supervised learning task that
involves multi-instance learning on gigapixel images. To date, radiology images
have proven to have CRC MSI information and efficient patient imaging
techniques. Different data modalities integration offers the opportunity to
increase the accuracy and robustness of MSI prediction. Despite the progress in
representation learning from the whole slide images (WSI) and exploring the
potential of making use of radiology data, CRC MSI prediction remains a
challenge to fuse the information from multiple data modalities (e.g.,
pathology WSI and radiology CT image). In this paper, we propose $M^{2}$Fusion:
a Bayesian-based multimodal multi-level fusion pipeline for CRC MSI. The
proposed fusion model $M^{2}$Fusion is capable of discovering more novel
patterns within and across modalities that are beneficial for predicting MSI
than using a single modality alone, as well as other fusion methods. The
contribution of the paper is three-fold: (1) $M^{2}$Fusion is the first
pipeline of multi-level fusion on pathology WSI and 3D radiology CT image for
MSI prediction; (2) CT images are the first time integrated into multimodal
fusion for CRC MSI prediction; (3) feature-level fusion strategy is evaluated
on both Transformer-based and CNN-based method. Our approach is validated on
cross-validation of 352 cases and outperforms either feature-level (0.8177 vs.
0.7908) or decision-level fusion strategy (0.8177 vs. 0.7289) on AUC score.
