CSIFNet: Deep multimodal network based on cellular spatial information fusion for survival prediction
Xiao Siqi, Zhiming Dai · 2023
Using multimodal data to construct models for cancer survival prediction is essential for the prognosis of patients. Most of existing methods do not consider the cellular spatial organization of tumors at the single-cell level. In this work, we present CSIFNet, a deep multimodal network based on cellular spatial information fusion. The multimodal data used includes genomics data, clinical variables data and cellular spatial data. Specifically, we construct a spatial information fusion module (SIFM) to learn the local interactions between tumor cells and the tumor microenvironment to obtain a more comprehensive high-level representation of tumor cellular spatial information. Experimental results show that our proposed method has better performance than the state-of-the-art survival prediction methods. The source code is available at https://github.com/heyhola/CSIFNet.