  Gigapixel medical images provide massive data, both morphological textures
and spatial information, to be mined. Due to the large data scale in histology,
deep learning methods play an increasingly significant role as feature
extractors. Existing solutions heavily rely on convolutional neural networks
(CNNs) for global pixel-level analysis, leaving the underlying local geometric
structure such as the interaction between cells in the tumor microenvironment
unexplored. The topological structure in medical images, as proven to be
closely related to tumor evolution, can be well characterized by graphs. To
obtain a more comprehensive representation for downstream oncology tasks, we
propose a fusion framework for enhancing the global image-level representation
captured by CNNs with the geometry of cell-level spatial information learned by
graph neural networks (GNN). The fusion layer optimizes an integration between
collaborative features of global images and cell graphs. Two fusion strategies
have been developed: one with MLP which is simple but turns out efficient
through fine-tuning, and the other with Transformer gains a champion in fusing
multiple networks. We evaluate our fusion strategies on histology datasets
curated from large patient cohorts of colorectal and gastric cancers for three
biomarker prediction tasks. Both two models outperform plain CNNs or GNNs,
reaching a consistent AUC improvement of more than 5% on various network
backbones. The experimental results yield the necessity for combining
image-level morphological features with cell spatial relations in medical image
analysis. Codes are available at https://github.com/yiqings/HEGnnEnhanceCnn.
