The DOI matches its Crossref record; 55 of the 56 references it cites were found in a registry or an index.
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For instance, Medical Subject Headings (MeSH) terms may be used to construct a drug-disease graph, from which latent representations of drugs and diseases are learned using various graph embedding algorithms, including DeepWalk and LINE (Section 2.1) [178].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [178]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21492 · Cited by 84,109
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
(2014) · Cited by 33,419
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
(2017) · Cited by 11,625
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582
Research works printed but not linked: 2
- shi, 0, Graph convolutional networks for cervical cell classification
- defferrard, 2016, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, Advances in neural information processing systems
Cited by
Sorted by citations
Items 201–223 of 223
Spatial morphoproteomic features predict disease states from tissue architectures
Hu, T., Ozturk, E., Allam, M., Nishkala, N., Kaushik, V., Goudy, S. L., Xu, Q., Mudd, P. A., Manthiram, K., & Coskun, A. F. (2025) · iScience · doi:10.1016/j.isci.2025.113204 · Cited by 3
Verdelho, M. R., Bernardino, A. J. M., & Barata, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-99568-2_14
Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion
Nishiyama, D., Miyoshi, H., Hashimoto, N., Ohshima, K., Hontani, H., Takeuchi, I., & Sakuma, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_31
Johnston, L., & Yu, Z. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111037
Kousha, A. A. (2025) · Journal of Biomedical Engineering and Biosciences · doi:10.11159/jbeb.2025.015
Gu, T.-Q., Wang, L., Wu, X., Zheng, Q., Qu, F., Chen, C., Di, G., Shao, Z., & Cao, A. (2026) · Clinical and Translational Medicine · doi:10.1002/ctm2.70644
Morphology-guided deep learning for nanoparticle agglomeration diagnostic assays
Jhawar, K., Chu, X., DeGrandchamp, J. B., Yin, Y., Peddibhotla, A. L., Banu, S., Hatta, F. N. N. M., Wang, Y., Török, P., & Wang, L. (2026) · Scientific Reports · doi:10.1038/s41598-026-45423-2
Weakly Supervised Segmentation and Context-Aware Classification in Computational Pathology
Lerousseau, M. (2022) · theses.fr (ABES) · doi:10.70675/8d10f89dz0f91z4e9dzb3a8zb5228b1172c1
Zheng, Q., Mei, H., Weng, X., Yang, R., Wang, K., Ni, X., Wu, J., Fan, J., Liu, T., Yuan, J., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01443-9
A multi-scale classification framework for pathological image diagnosis
Jiang, P., Xu, Y., Zhang, M., Wang, C., & Lu, N. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02339-8
A hybrid CNN-GCN framework for interpretable Alzheimer’s disease diagnosis from MRI scans
Islam, J., Lin, F., & Lin, C. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.05.017
Multi-diagnosis multi-instance learning for auxiliary gene mutation diagnosis in whole slide images
Liu, A., Liu, Y., Li, W., Liu, C., Qin, X., Li, Z., Chen, Y., Jin, L., & Liang, B. (2026) · Pathology - Research and Practice · doi:10.1016/j.prp.2026.156576
ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis
Miccinilli, H., & Piazza, T. D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38172-9_13
Scalable 3D cell-interaction analysis via supercell graphs for prostate cancer risk stratificationPreprint
Zhao, Y., Chow, S. S. L., Yan, R., Brenes, D., Serafin, R., Almagro-Pérez, C., Song, A. H., Lal, P., Chan, E., Downes, M. R., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.07.736891
Midhunchakkaravarthy, D., Muni Nagamani, G., & Lakshman Narayana, V. (2026) · Journal of Innovative Image Processing · doi:10.36548/jiip.2026.3.016
Graph Unlearning for MLaaS: Toward Flexible Privacy Adjustment via Influenced Subgraph
Li, Y., Zhao, Y., Tan, Q., Wang, Y.-G., Wang, L., Wei, T., Zhu, M., Xu, K., & Zhao, Y.-J. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3715465
Wang, S., Xu, X., Zhang, L., Du, H., Chen, Y., & Mei, W. (2026) · International Conference on Image Processing and Artificial Intelligence (ICIPAI) · doi:10.1109/icipai70034.2026.11605614
Patch-Level Tissue Context Improves Learning from Frozen Pathology Foundation Model Embeddings
Dip, S. A., & Zhang, L. (2026) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3807503.3820870
Schüllerqvist, O. E., Baumann, J., Lindblad, J., Nordling, L., Mezheyeuski, A., Micke, P., & Sladoje, N. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31936-4_6
ECHO: A unified framework for efficient cell-graph construction for breast histology classification
Tommasino, C., Russo, C., & Rinaldi, A. M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111294
Hierarchical graph networks for breast cancer subtype classification
Rinaldi, A. M., Russo, C., & Tommasino, C. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-026-01259-y
Habijan, M., Nakić, P., Marinčić, D., & Samardžić, J. (2026) · International Symposium ELMAR (ELMAR) · doi:10.1109/elmar71231.2026.11712213
Metadata
- Authors
- Zhou, Y., Graham, S., Alemi Koohbanani, N., Shaban, M., Heng, P.-A., & Rajpoot, N.
- Affiliations
- Chinese University of Hong Kong (HK)
- University of Warwick (GB)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 388-398
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2971692138
- Cited by
- 225
- References
- 56
- Citations by year
- 2026: 18 · 2025: 31 · 2024: 51 · 2023: 46 · 2022: 39 · 2021: 26 · 2020: 13 · 2019: 1
- FWCI
- 7.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- University of Warwick
- Chinese University of Hong Kong
- Medical Research Council (MR/P015476/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- histological image analysis, graph-based image representation, gland formation, multi-scale feature fusion, node sampling, nuclear features
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
