mlsanityData to 7 Oct 2026

CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images

arXiv 1909.01068 ↗First posted 3 Sep 2019eess.IV cs.CVcited by 57

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Abstract (arXiv)

Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tissue micro-environment by assessing the cell-level information along with the morphology of the gland. However, current automated methods for CRC grading typically utilise small image patches and therefore fail to incorporate the entire tissue micro-architecture for grading purposes. To overcome the challenges of CRC grading, we present a novel cell-graph convolutional neural network (CGC-Net) that converts each large histology image into a graph, where each node is represented by a nucleus within the original image and cellular interactions are denoted as edges between these nodes according to node similarity. The CGC-Net utilises nuclear appearance features in addition to the spatial location of nodes to further boost the performance of the algorithm. To enable nodes to fuse multi-scale information, we introduce Adaptive GraphSage, which is a graph convolution technique that combines multi-level features in a data-driven way. Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity. We show that modeling the image as a graph enables us to effectively consider a much larger image (around 16× larger) than traditional patch-based approaches and model the complex structure of the tissue micro-environment. We construct cell graphs with an average of over 3,000 nodes on a large CRC histology image dataset and report state-of-the-art results as compared to recent patch-based as well as contextual patch-based techniques, demonstrating the effectiveness of our method.

Key facts. Cited by 57 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2019.

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Citing papers per year: 2021: 11; 2022: 9; 2023: 10; 2024: 14; 2025: 7; 2026: 6.

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  1. 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
  2. 2023Artificial Intelligence for Digital and Computational Pathologycited by 106
  3. 2022A graph-transformer for whole slide image classificationcited by 84
  4. 2021Graph Representation Learning in Biomedicinecited by 79
  5. 2024Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysiscited by 66
  6. 2021Hierarchical Graph Representations in Digital Pathologycited by 58
  7. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  8. 2023OCELOT: Overlapped Cell on Tissue Dataset for Histopathologycited by 29
  9. 2022Vision Transformers in Medical Computer Vision -- A Contemplative Retrospectioncited by 22
  10. 2022ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classificationcited by 21
  11. 2021HistoCartography: A Toolkit for Graph Analytics in Digital Pathologycited by 20
  12. 2022A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protectioncited by 20
38 more
  1. 2022An Aggregation of Aggregation Methods in Computational Pathologycited by 20
  2. 2023Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Reviewcited by 20
  3. 2023Structure Embedded Nucleus Classification for Histopathology Imagescited by 14
  4. 2024Graph Neural Networks in Histopathology: Emerging Trends and Future Directionscited by 13
  5. 2024Cell Graph Transformer for Nuclei Classificationcited by 9
  6. 2022Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applicationscited by 7
  7. 2023Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Reviewcited by 7
  8. 2022GCNET: graph-based prediction of stock price movement using graph convolutional networkcited by 6
  9. 2023VOLTA: an Environment-Aware Contrastive Cell Representation Learning for Histopathologycited by 6
  10. 2021KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligencecited by 5
  11. 2021Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classificationcited by 5
  12. 2021Hybrid Supervision Learning for Pathology Whole Slide Image Classificationcited by 5
  13. 2023Multi-scale Efficient Graph-Transformer for Whole Slide Image Classificationcited by 5
  14. 2024GRASP: GRAph-Structured Pyramidal Whole Slide Image Representationcited by 5
  15. 2021Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flowscited by 4
  16. 2022Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approachescited by 4
  17. 2024DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Imagescited by 4
  18. 2023A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasiacited by 3
  19. 2024Focus on Focus: Focus-oriented Representation Learning and Multi-view Cross-modal Alignment for Glioma Gradingcited by 3
  20. 2021Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysiscited by 2
  21. 2022A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphscited by 2
  22. 2023Heterogeneous graphs model spatial relationships between biological entities for breast cancer diagnosiscited by 2
  23. 2024Cross-domain and Cross-dimension Learning for Image-to-Graph Transformerscited by 2
  24. 2025TopoImages: Incorporating Local Topology Encoding into Deep Learning Models for Medical Image Classificationcited by 2
  25. 2026GrapHist: Graph Self-Supervised Learning for Histopathologycited by 2
  26. 2021Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasmscited by 1
  27. 2024HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysiscited by 1
  28. 2024Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classificationcited by 1
  29. 2024xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancercited by 1
  30. 2025A Graph-Based Framework for Interpretable Whole Slide Image Analysiscited by 1
  31. 2023Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classificationcited by 0
  32. 2024HistoSegCap: Capsules for Weakly-Supervised Semantic Segmentation of Histological Tissue Type in Whole Slide Imagescited by 0
  33. 2024Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progressioncited by 0
  34. 2024C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Gradingcited by 0
  35. 2024A Survey on Cell Nuclei Instance Segmentation and Classification: Leveraging Context and Attentioncited by 0
  36. 2025Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusioncited by 0
  37. 2025MIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learningcited by 0
  38. 2025Hierarchical Graph Feature Enhancement with Adaptive Frequency Modulation for Visual Recognitioncited by 0

50 most cited of 57 All 57 (JSON)