  In this paper we propose C2G-Net, a pipeline for image classification that
exploits the morphological properties of images containing a large number of
similar objects like biological cells. C2G-Net consists of two components: (1)
Cell2Grid, an image compression algorithm that identifies objects using
segmentation and arranges them on a grid, and (2) DeepLNiNo, a CNN architecture
with less than 10,000 trainable parameters aimed at facilitating model
interpretability. To test the performance of C2G-Net we used multiplex
immunohistochemistry images for predicting relapse risk in colon cancer.
Compared to conventional CNN architectures trained on raw images, C2G-Net
achieved similar prediction accuracy while training time was reduced by 85% and
its model was is easier to interpret.
