  Digital histology images are amenable to the application of convolutional
neural network (CNN) for analysis due to the sheer size of pixel data present
in them. CNNs are generally used for representation learning from small image
patches (e.g. 224x224) extracted from digital histology images due to
computational and memory constraints. However, this approach does not
incorporate high-resolution contextual information in histology images. We
propose a novel way to incorporate larger context by a context-aware neural
network based on images with a dimension of 1,792x1,792 pixels. The proposed
framework first encodes the local representation of a histology image into high
dimensional features then aggregates the features by considering their spatial
organization to make a final prediction. The proposed method is evaluated for
colorectal cancer grading and breast cancer classification. A comprehensive
analysis of some variants of the proposed method is presented. Our method
outperformed the traditional patch-based approaches, problem-specific methods,
and existing context-based methods quantitatively by a margin of 3.61%. Code
and dataset related information is available at this link:
https://tia-lab.github.io/Context-Aware-CNN
