  Integrating the different data modalities of cancer patients can
significantly improve the predictive performance of patient survival. However,
most existing methods ignore the simultaneous utilization of rich semantic
features at different scales in pathology images. When collecting multimodal
data and extracting features, there is a likelihood of encountering
intra-modality missing data, introducing noise into the multimodal data. To
address these challenges, this paper proposes a new end-to-end framework,
FORESEE, for robustly predicting patient survival by mining multimodal
information. Specifically, the cross-fusion transformer effectively utilizes
features at the cellular level, tissue level, and tumor heterogeneity level to
correlate prognosis through a cross-scale feature cross-fusion method. This
enhances the ability of pathological image feature representation. Secondly,
the hybrid attention encoder (HAE) uses the denoising contextual attention
module to obtain the contextual relationship features and local detail features
of the molecular data. HAE's channel attention module obtains global features
of molecular data. Furthermore, to address the issue of missing information
within modalities, we propose an asymmetrically masked triplet masked
autoencoder to reconstruct lost information within modalities. Extensive
experiments demonstrate the superiority of our method over state-of-the-art
methods on four benchmark datasets in both complete and missing settings.
