mlsanityData to 14 Oct 2026

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

arXiv 1907.09478 ↗First posted 22 Jul 2019eess.IV cs.LG stat.MLcited by 39

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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

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

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Citing papers per year: 2021: 14; 2022: 8; 2023: 7; 2024: 6; 2025: 3; 2026: 1.

39 papers.

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  1. 2023Artificial Intelligence for Digital and Computational Pathologycited by 108
  2. 2021Lizard: A Large-Scale Dataset for Colonic Nuclear Instance Segmentation and Classificationcited by 89
  3. 2021Hierarchical Graph Representations in Digital Pathologycited by 59
  4. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  5. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  6. 2022One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classificationcited by 38
  7. 2023CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Countingcited by 33
  8. 2021Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphscited by 22
  9. 2022Vision Transformers in Medical Computer Vision -- A Contemplative Retrospectioncited by 22
  10. 2024Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosiscited by 21
  11. 2021HistoCartography: A Toolkit for Graph Analytics in Digital Pathologycited by 20
  12. 2022An Aggregation of Aggregation Methods in Computational Pathologycited by 20
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  1. 2022HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology imagescited by 17
  2. 2024Knowledge-enhanced Visual-Language Pretraining for Computational Pathologycited by 14
  3. 2024Boosting Medical Image Classification with Segmentation Foundation Modelcited by 8
  4. 2021FedDropoutAvg: Generalizable federated learning for histopathology image classificationcited by 7
  5. 2021Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classificationcited by 5
  6. 2021Hybrid Supervision Learning for Pathology Whole Slide Image Classificationcited by 5
  7. 2022SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesiscited by 5
  8. 2021A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinomacited by 3
  9. 2021An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classificationcited by 3
  10. 2021Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddingscited by 2
  11. 2021Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysiscited by 2
  12. 2021Towards Launching AI Algorithms for Cellular Pathology into Clinical & Pharmaceutical Orbitscited by 2
  13. 2023ColonNeRF: High-Fidelity Neural Reconstruction of Long Colonoscopycited by 2
  14. 2025TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classificationcited by 2
  15. 2021Segmentation of Lungs COVID Infected Regions by Attention Mechanism and Synthetic Datacited by 1
  16. 2022Is the U-Net Directional-Relationship Aware?cited by 1
  17. 2023Centroid-aware feature recalibration for cancer grading in pathology imagescited by 1
  18. 2024HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysiscited by 1
  19. 2024Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answeringcited by 1
  20. 2026ARTEMIS: Agent-guided Reliability-aware Temporal Mask Evolution for Imperfectly Supervised Video Polyp Segmentationcited by 1
  21. 2022Deep Learning based Prediction of MSI using MMR Markers in Colorectal Cancercited by 0
  22. 2023DeepFEL: Deep Fastfood Ensemble Learning for Histopathology Image Analysiscited by 0
  23. 2023Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelinescited by 0
  24. 2023DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecturecited by 0
  25. 2024C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Gradingcited by 0
  26. 2025Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learningcited by 0
  27. 2025Evaluating Cell Type Inference in Vision Language Models Under Varying Visual Contextcited by 0