# Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images

arXiv: https://arxiv.org/abs/1907.09478 · HTML: https://mlsanity.com/paper/1907.09478/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 30 of 39)

- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classification](https://mlsanity.com/paper/2108.11195/) (2021)
- [Hierarchical Graph Representations in Digital Pathology](https://mlsanity.com/paper/2102.11057/) (2021)
- [Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images](https://mlsanity.com/paper/2204.12454/) (2022)
- [A Survey on Graph-Based Deep Learning for Computational Histopathology](https://mlsanity.com/paper/2107.00272/) (2021)
- [One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification](https://mlsanity.com/paper/2203.00077/) (2022)
- [CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting](https://mlsanity.com/paper/2303.06274/) (2023)
- [Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs](https://mlsanity.com/paper/2103.03129/) (2021)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosis](https://mlsanity.com/paper/2412.13126/) (2024)
- [HistoCartography: A Toolkit for Graph Analytics in Digital Pathology](https://mlsanity.com/paper/2107.10073/) (2021)
- [An Aggregation of Aggregation Methods in Computational Pathology](https://mlsanity.com/paper/2211.01256/) (2022)
- [HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images](https://mlsanity.com/paper/2209.00729/) (2022)
- [Knowledge-enhanced Visual-Language Pretraining for Computational Pathology](https://mlsanity.com/paper/2404.09942/) (2024)
- [Boosting Medical Image Classification with Segmentation Foundation Model](https://mlsanity.com/paper/2406.11026/) (2024)
- [FedDropoutAvg: Generalizable federated learning for histopathology image classification](https://mlsanity.com/paper/2111.13230/) (2021)
- [Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification](https://mlsanity.com/paper/2106.15299/) (2021)
- [Hybrid Supervision Learning for Pathology Whole Slide Image Classification](https://mlsanity.com/paper/2107.00934/) (2021)
- [SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis](https://mlsanity.com/paper/2209.02412/) (2022)
- [A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma](https://mlsanity.com/paper/2104.12862/) (2021)
- [An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification](https://mlsanity.com/paper/2106.02864/) (2021)
- [Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings](https://mlsanity.com/paper/2102.07611/) (2021)
- [Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis](https://mlsanity.com/paper/2104.00669/) (2021)
- [Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbits](https://mlsanity.com/paper/2112.09496/) (2021)
- [ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopy](https://mlsanity.com/paper/2312.02015/) (2023)
- [TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classification](https://mlsanity.com/paper/2508.01574/) (2025)
- [Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Data](https://mlsanity.com/paper/2108.08895/) (2021)
- [Is the U-Net Directional-Relationship Aware?](https://mlsanity.com/paper/2207.02574/) (2022)
- [Centroid-aware feature recalibration for cancer grading in pathology images](https://mlsanity.com/paper/2307.13947/) (2023)
- [HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis](https://mlsanity.com/paper/2408.08847/) (2024)

## Abstract (arXiv)

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