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.
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
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?
MeSH terms may be used to construct a drug-disease graph, from which latent representations of drugs and diseases are learned using graph embedding algorithms including DeepWalk and LINE.
“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.
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 · see citing works
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 · see citing works
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 · see citing works
(2014) · Cited by 33,419 · see citing works
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
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 · see citing works
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 · see citing works
(2017) · Cited by 11,625 · see citing works
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 · see citing works
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 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)Preprint
Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.09375 · Cited by 6,912 · see citing works
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,977 · see citing works
Spectral Networks and Locally Connected Networks on Graphs
Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983 · see citing works
Fast Graph Representation Learning with PyTorch GeometricPreprint
Fey, M., & Lenssen, J. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02428 · Cited by 3,403 · see citing works
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
Li, Q., Han, Z., & Wu, X.-M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11604 · Cited by 2,872 · see citing works
Learning Convolutional Neural Networks for GraphsPreprint
Niepert, M., Ahmed, M., & Kutzkov, K. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1605.05273 · Cited by 2,311 · see citing works
A new model for learning in graph domains
Gori, M., Monfardini, G., & Scarselli, F. (2006) · Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. · doi:10.1109/ijcnn.2005.1555942 · Cited by 2,075 · see citing works
An End-to-End Deep Learning Architecture for Graph Classification
Zhang, M., Cui, Z., Neumann, M., & Chen, Y. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11782 · Cited by 1,525 · see citing works
Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A. S., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101563 · Cited by 1,445 · see citing works
Self-citationRepresentation Learning on Graphs with Jumping Knowledge NetworksPreprint
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., & Jegelka, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03536 · Cited by 1,379 · see citing works
Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W. L., & Leskovec, J. (2018) · neural information processing systems · Cited by 1,353 · see citing works
Multi-Label Image Recognition With Graph Convolutional Networks
Chen, Z.-M., Wei, X.-S., Wang, P., & Guo, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00532 · Cited by 1,339 · see citing works
Prognostic Factors in Colorectal Cancer
Compton, C. C., FIELDING, L. P., Burgart, L. J., Conley, B. A., Cooper, H. S., Hamilton, S. R., Hammond, M. E. H., Henson, D. E., Hutter, R. V. P., Nagle, R. B., et al. (2000) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2000-124-0979-pficc · Cited by 1,260 · see citing works
Pathology and genetics of tumours of the digestive system
Hamilton, S. R., & Aaltonen, L. (2000) · Histopathology · doi:10.1046/j.1365-2559.2001.01219.x · Cited by 1,213 · see citing works
Classification of breast cancer histology images using Convolutional Neural Networks
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177544 · Cited by 1,017 · see citing works
Breast cancer histopathological image classification using Convolutional Neural Networks
Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727519 · Cited by 996 · see citing works
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
Zana, F., & Klein, J. C. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.931095 · Cited by 871 · see citing works
The farthest point strategy for progressive image sampling
Eldar, Y., Lindenbaum, M., Porat, M., & Zeevi, Y. Y. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.623193 · Cited by 698 · see citing works
Neural Network for Graphs: A Contextual Constructive Approach
Micheli, A. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2010350 · Cited by 639 · see citing works
Detecting Cancer Metastases on Gigapixel Pathology Images
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv · doi:10.48550/arxiv.1703.02442 · Cited by 506 · see citing works
Graham, S., Chen, H., Gamper, J., Dou, Q., Heng, P., Snead, D. R. J., Tsang, Y. W., & Rajpoot, N. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.12.001 · Cited by 342 · see citing works
Self-citationPrognostic Factors in Prostate Cancer
Bostwick, D. G., Grignon, D. J., Hammond, M. E., Amin, M. B., Cohen, M., Crawford, D., Gospadarowicz, M., Kaplan, R. S., Miller, D. S., Montironi, R., et al. (2000) · Archives of Pathology & Laboratory Medicine · doi:10.5858/2000-124-0995-pfipc · Cited by 314 · see citing works
CIA-Net: Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation
Zhou, Y., Onder, O. F., Dou, Q., Tsougenis, E., Chen, H., & Heng, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_53 · Cited by 229 · see citing works
Self-citationGraph U-NetsPreprint
Gao, H., & Ji, S. (2019) · arXiv (Cornell University) · Cited by 206 · see citing works
Computerized classification of intraductal breast lesions using histopathological images
Dündar, M., Badve, S., Bilgin, G., Raykar, V. C., Jain, R., Sertel, O., & Gürcan, M. N. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2110648 · Cited by 194 · see citing works
Context-aware convolutional neural network for grading of colorectal cancer histology images
Shaban, M., Awan, R., Fraz, M. M., Azam, A., Tsang, Y.-W., Snead, D., & Rajpoot, N. M. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2971006 · Cited by 179 · see citing works
Self-citationGeçer, B., Aksoy, S., Mercan, E., Shapiro, L. G., Weaver, D. L., & Elmore, J. G. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.022 · Cited by 176 · see citing works
Diamond, J., Anderson, N. H., Bartels, P. H., Montironi, R., & Hamilton, P. W. (2004) · Human Pathology · doi:10.1016/j.humpath.2004.05.010 · Cited by 166 · see citing works
Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images
Awan, R., Sirinukunwattana, K., Epstein, D. B. A., Jefferyes, S. D. R., Qidwai, U. A., Aftab, Z., Mujeeb, I., Snead, D. R. J., & Rajpoot, N. (2017) · Scientific Reports · doi:10.1038/s41598-017-16516-w · Cited by 161 · see citing works
Self-citationCruz-Roa, Á., Gilmore, H. L., Basavanhally, A. N., Feldman, M. D., Ganesan, S., Shih, N. N. C., Tomaszewski, J., Madabhushi, A., & Gonzalez, F. A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196828 · Cited by 151 · see citing works
Prostate cancer grading: Gland segmentation and structural features
Nguyen, K., Sabata, B., & Jain, A. K. (2011) · Pattern Recognition Letters · doi:10.1016/j.patrec.2011.10.001 · Cited by 114 · see citing works
Cell-Graph Mining for Breast Tissue Modeling and Classification
Bilgin, C. C., Gündüz-Demir, Ç., Nagi, C. S., & Yener, B. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353540 · Cited by 102 · see citing works
Augmented cell-graphs for automated cancer diagnosis
Demir, C., Gultekin, S. H., & Yener, B. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1100 · Cited by 62 · see citing works
ECM-aware cell-graph mining for bone tissue modeling and classification
Bilgin, C. C., Bullough, P. G., Plopper, G. E., & Yener, B. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0153-2 · Cited by 54 · see citing works
Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer
Sirinukunwattana, K., Snead, D. R. J., Epstein, D. B. A., Aftab, Z., Mujeeb, I., Tsang, Y. W., Cree, I. A., & Rajpoot, N. (2018) · Scientific Reports · doi:10.1038/s41598-018-31799-3 · Cited by 52 · see citing works
Self-citationWeyn, B., van de Wouwer, G., Kumar‐Singh, S., van Daele, A., Scheunders, P., Marck, E. V., & Jacob, W. A. (1999) · Cytometry · doi:10.1002/(sici)1097-0320(19990101)35:1 · Cited by 48 · see citing works
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study
Sirinukunwattana, K., Alham, N. K., Verrill, C. L., & Rittscher, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_22 · Cited by 47 · see citing works
Classification of lung cancer histology images using patch-level summary statistics
Khurram, S. A., Graham, S., Shaban, M., Qaiser, T., & Rajpoot, N. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2293855 · Cited by 45 · see citing works
Self-citationComputer-aided classification of breast cancer nuclei
Schnorrenberg, F., Pattichis, C. S., Schizas, C. N., Kyriacou, K. C., & Vassiliou, M. (1996) · Technology and Health Care · doi:10.3233/thc-1996-4203 · Cited by 42 · see citing works
Yener, B. (2016) · Communications of the ACM · doi:10.1145/2960404 · Cited by 38 · see citing works
Follicular lymphoma grading using cell-graphs and multi-scale feature analysis
Oztan, B., Kong, H., Gürcan, M. N., & Yener, B. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.911360 · Cited by 35 · see citing works
Cellular Community Detection for Tissue Phenotyping in Histology Images
Javed, S., Fraz, M. M., Epstein, D., Snead, D. R. J., & Rajpoot, N. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00949-6_15 · Cited by 18 · see citing works
Self-citationChen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6
Flagged first, then by citations
1 of the 56 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Michaël Defferrard, Xavier Bresson, and Pierre Van- dergheynst. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems.
- Shi Jun, Wang Ruoyu, Zheng Yushan, Jiang Zhiguo, and Yu Lanlan. Graph convolutional networks for cervical cell classification. https://openreview.net/pdf?id= S1gX_tlc-S.
- Stephen J Keenan, James Diamond, W Glenn McCluggage, Hoshang Bharucha, Deborah Thompson, Peter H Bartels, and Peter W Hamilton. An automated machine vision system for the histological grading of cervical intraepithelial neopla- sia (cin). The Journal of Pathology, 192(3):351-362, 2000.
- Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic dif- ferentiation in PyTorch. In NIPS Autodiff Workshop, 2017.
- Bulent Yener. Cell-graphs: image-driven modeling of structure-function relationship. Communications of the ACM, 60(1):74-84, 2016.
Cited by
Sorted by citations
Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., & Mahmood, F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3021387 · Cited by 729 · see citing works
Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works
Vision Transformers in medical computer vision—A contemplative retrospection
Parvaiz, A., Khalid, M. A., Zafar, R., Ameer, H., Ali, M. U., & Fraz, M. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106126 · Cited by 288 · see citing works
Federated learning for computational pathology on gigapixel whole slide images
Lu, M. Y., Chen, R. J., Kong, D., Lipkova, J., Singh, R., Williamson, D. F. K., Chen, T. Y., & Mahmood, F. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102298 · Cited by 267 · see citing works
A Graph-Transformer for Whole Slide Image Classification
Zheng, Y., Gindra, R. H., Green, E. J., Burks, E. J., Betke, M., Beane, J. E., & Kolachalama, V. B. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3176598 · Cited by 255 · see citing works
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review
Alharbi, F., & Vakanski, A. (2023) · Bioengineering · doi:10.3390/bioengineering10020173 · Cited by 252 · see citing works
Chen, R. J., Lu, M. Y., Shaban, M., Chen, C., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87237-3_33 · Cited by 176 · see citing works
Self-citationHierarchical graph representations in digital pathology
Pati, P., Jaume, G., Foncubierta–Rodríguez, A., Feroce, F., Anniciello, A. M., Scognamiglio, G., Brancati, N., Fiche, M., Dubruc, E., Riccio, D., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102264 · Cited by 168 · see citing works
Wu, Z., Trevino, A. E., Wu, E., Swanson, K., Kim, H. J., D’Angio, H. B., Preska, R., Charville, G. W., Dalerba, P. D., Egloff, A. M., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00951-w · Cited by 154 · see citing works
A survey on graph-based deep learning for computational histopathology
Ahmedt‐Aristizabal, D., Armin, M. A., Denman, S., Fookes, C., & Petersson, L. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.102027 · Cited by 150 · see citing works
Triple U-net: Hematoxylin-aware nuclei segmentation with progressive dense feature aggregation
Zhao, B., Chen, X., Li, Z., Yu, Z., Yao, S., Yan, L., Wang, Y., Liu, Z., Liang, C., & Han, C. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101786 · Cited by 144 · see citing works
Waqas, A., Bui, M. M., Glassy, E. F., Naqa, I. E., Borkowski, P., Borkowski, A., & Rasool, G. (2023) · Laboratory Investigation · doi:10.1016/j.labinv.2023.100255 · Cited by 149 · see citing works
Cervical cell classification with graph convolutional network
Shi, J., Wang, R., Zheng, Y., Jiang, Z., Zhang, H., & Yu, L. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105807 · Cited by 129 · see citing works
Lee, Y., Park, J. H., Oh, S., Shin, K., Sun, J., Jung, M., Lee, C., Kim, H., Chung, J.-H., Moon, K. C., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00923-0 · Cited by 124 · see citing works
Li, J., Chen, Y., Chu, H., Sun, Q., Guan, T. M., Han, A., & He, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01076 · Cited by 109 · see citing works
Principles and challenges of modeling temporal and spatial omics data
Velten, B., & Stegle, O. (2023) · Nature Methods · doi:10.1038/s41592-023-01992-y · Cited by 90 · see citing works
ResGNet-C: A graph convolutional neural network for detection of COVID-19
Yu, X., Lu, S., Guo, L., Wang, S., & Zhang, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.144 · Cited by 82 · see citing works
Quantifying Explainers of Graph Neural Networks in Computational Pathology
Jaume, G., Pati, P., Bozorgtabar, B., Foncubierta–Rodríguez, A., Anniciello, A. M., Feroce, F., Rau, T. T., Thiran, J., Gabrani, M., & Göksel, O. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00801 · Cited by 82 · see citing works
A survey on artificial intelligence in histopathology image analysis
Abdelsamea, M. M., Zidan, U., Senousy, Z., Gaber, M. M., Rakha, E. A., & Ilyas, M. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1474 · Cited by 80 · see citing works
Node-aligned Graph Convolutional Network for Whole-slide Image Representation and Classification
Guan, Y., Zhang, J., Tian, K., Yang, S., Dong, P., Xiang, J., Yang, W., Huang, J., Zhang, Y., & Han, X. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01825 · Cited by 74 · see citing works
Zhao, S., Chen, D.-P., Fu, T., Yang, J., Ma, D., Zhu, X.-Z., Wang, X.-X., Jiao, Y., Jin, X., Xiao, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42504-y · Cited by 74 · see citing works
Gao, Z., Lu, Z., Wang, J., Ying, S., & Shi, J. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3153671 · Cited by 72 · see citing works
Pati, P., Jaume, G., Fernandes, L. A., Foncubierta–Rodríguez, A., Feroce, F., Anniciello, A. M., Scognamiglio, G., Brancati, N., Riccio, D., Bonito, M. D., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60365-6_20 · Cited by 71 · see citing works
Weakly Supervised Prostate Tma Classification Via Graph Convolutional Networks
Wang, J., Chen, R. J., Lu, M. Y., Baras, A. S., & Mahmood, F. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098534 · Cited by 69 · see citing works
Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends
Gogoshin, G., & Rodin, A. S. (2023) · Cancers · doi:10.3390/cancers15245858 · Cited by 64 · see citing works
Fischman, S., Pérez‐Ánker, J., Tognetti, L., Naro, A. D., Suppa, M., Cinotti, É., Viel, T., Monnier, J., Rubegni, P., del Mármol, V., et al. (2022) · Scientific Reports · doi:10.1038/s41598-021-04395-1 · Cited by 63 · see citing works
Al-Thelaya, K., Gilal, N. U., Alzubaidi, M. S., Majeed, F., Agus, M., Schneider, J., & Househ, M. S. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100335 · Cited by 63 · see citing works
Zhou, P., Cao, Y., Li, M., Ma, Y., Chen, C., Gan, X., Wu, J., Lv, X., & Chen, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-18879-1 · Cited by 64 · see citing works
GCNET: Graph-based prediction of stock price movement using graph convolutional network
Jafari, A., & Haratizadeh, S. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105452 · Cited by 60 · see citing works
Alnaggar, O. A. M. F., Jagadale, B. N., Saif, M. A. N., Ghaleb, O. A. M., Ahmed, A. A. Q., Aqlan, H. A. A., & Al-ariki, H. D. E. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10814-2 · Cited by 67 · see citing works
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Oliveira, S. P., Neto, P. C., Fraga, J. B. D. P., Montezuma, D., Monteiro, A., Monteiro, J., Ribeiro, L., Gonçalves, S., Pinto, I., & Cardoso, J. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-93746-z · Cited by 58 · see citing works
Graph representation learning for single-cell biology
Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.008 · Cited by 55 · see citing works
Stegmüller, T., Bozorgtabar, B., Spahr, A., & Thiran, J. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00611 · Cited by 54 · see citing works
Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging
Raju, A., Yao, J., Haq, M. M., Jonnagaddala, J., & Huang, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_51 · Cited by 53 · see citing works
Graph neural networks in histopathology: Emerging trends and future directions
Brussee, S., Buzzanca, G., Schrader, A. M. R., & Kers, J. (2025) · Medical Image Analysis · doi:10.1016/j.media.2024.103444 · Cited by 59 · see citing works
An aggregation of aggregation methods in computational pathology
Bilal, M., Jewsbury, R., Wang, R., AlGhamdi, H. M., Asif, A., Eastwood, M., & Rajpoot, N. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102885 · Cited by 49 · see citing works
Self-citationShephard, A., Bashir, R. M. S., Mahmood, H., Jahanifar, M., Minhas, F., Raza, S. E. A., McCombe, K. D., Craig, S. G., James, J. A., Brooks, J. M., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00624-8 · Cited by 47 · see citing works
Self-citationGraph Neural Networks: Architectures, Applications, and Future Directions
Ponzi, V., & Napoli, C. (2025) · IEEE Access · doi:10.1109/access.2025.3558752 · Cited by 48 · see citing works
Xiao, X., Kong, Y., Li, R.-H., Wang, Z., & Lu, H. J. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103040 · Cited by 44 · see citing works
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Ryu, J., Puche, A. V., Shin, J., Park, S., Brattoli, B., Lee, J., Jung, W., Cho, S. I., Paeng, K., Ock, C., et al. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02289 · Cited by 44 · see citing works
Toward human-level concept learning: Pattern benchmarking for AI algorithms
Holzinger, A., Saranti, A., Angerschmid, A., Finzel, B., Schmid, U., & Mueller, H. (2023) · Patterns · doi:10.1016/j.patter.2023.100788 · Cited by 50 · see citing works
Applications of Neural Networks in Biomedical Data Analysis
Weiss, R., Karimijafarbigloo, S., Roggenbuck, D., & Rödiger, S. (2022) · Biomedicines · doi:10.3390/biomedicines10071469 · Cited by 43 · see citing works
Tajeddin, N. Z., Jahanifar, M., Bilal, M., Eastwood, M., & Rajpoot, N. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103071 · Cited by 38 · see citing works
Self-citationShi, J., Tang, L., Li, Y., Zhang, X., Gao, Z., Zheng, Y., Wang, C., Gong, T., & Li, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3273236 · Cited by 37 · see citing works
Clinical applications of graph neural networks in computational histopathology: A review
Meng, X., & Zou, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107201 · Cited by 38 · see citing works
Vanea, C., Džigurski, J., Rukins, V., Dodi, O., Siigur, S., Salumäe, L., Meir, K. S., Parks, W. T., Hochner‐Celnikier, D., Fraser, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46986-2 · Cited by 36 · see citing works
Visualization for Histopathology Images using Graph Convolutional Neural Networks
Sureka, M., Patil, A., Anand, D., & Sethi, A. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00060 · Cited by 36 · see citing works
Gao, R., Yuan, X., Ma, Y., Wei, T., Johnston, L., Shao, Y., Lv, W., Zhu, T., Zhang, Y., Zheng, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101536 · Cited by 45 · see citing works
Deep learning powers cancer diagnosis in digital pathology
He, Y., Zhao, H. P., & Wong, S. T. C. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101820 · Cited by 36 · see citing works
Gao, Z., Mao, A., Wu, K., Li, Y., Zhao, L., Zhang, X., Wu, J., Yu, L., Xing, C., Gong, T., et al. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3248559 · Cited by 34 · see citing works
HistoCartography: A Toolkit for Graph Analytics in Digital PathologyPreprint
Jaume, G., Pati, P., Anklin, V., Foncubierta–Rodríguez, A., & Gabrani, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10073 · Cited by 34 · see citing works
Systematic inference of super-resolution cell spatial profiles from histology images
Zhang, P., Gao, C., Zhang, Z., Yuan, Z., Zhang, Q., Zhang, P., Du, S., Zhou, W., Li, Y., & Li, S. (2025) · Nature Communications · doi:10.1038/s41467-025-57072-6 · Cited by 32 · see citing works
Senousy, Z., Abdelsamea, M. M., Mohamed, M. M., & Gaber, M. M. (2021) · Entropy · doi:10.3390/e23050620 · Cited by 34 · see citing works
SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images
Deshpande, S., Minhas, F., Graham, S., & Rajpoot, N. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102337 · Cited by 32 · see citing works
Self-citationGraham, S., Minhas, F., Bilal, M., Ali, M., Tsang, Y. W., Eastwood, M., Wahab, N., Jahanifar, M., Hero, E., Dodd, K., et al. (2023) · Gut · doi:10.1136/gutjnl-2023-329512 · Cited by 32 · see citing works
Self-citationJoint categorical and ordinal learning for cancer grading in pathology images
Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102206 · Cited by 30 · see citing works
Colorectal multi-class image classification using deep learning models
Raju, M. S. N., & Rao, B. S. (2022) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v11i1.3299 · Cited by 29 · see citing works
Wang, H., Huang, G., Zhao, Z., Liang Cheng, C., Juncker‐Jensen, A., Nagy, M. L., Lü, X., Zhang, X., & Chen, D. Z. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3249343 · Cited by 30 · see citing works
Towards Explainable Graph Representations in Digital PathologyPreprint
Jaume, G., Pati, P., Foncubierta–Rodríguez, A., Feroce, F., Scognamiglio, G., Anniciello, A. M., Thiran, J., Göksel, O., & Gabrani, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00311 · Cited by 29 · see citing works
Multi-Scale Efficient Graph-Transformer for Whole Slide Image Classification
Ding, S., Li, J., Wang, J., Ying, S., & Shi, J. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3317067 · Cited by 29 · see citing works
Breast Cancer Classification From Digital Pathology Images via Connectivity-Aware Graph Transformer
Wang, K., Zheng, F., Cheng, L., Dai, H., Dou, Q., & Qin, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3381239 · Cited by 28 · see citing works
Opportunities and challenges of graph neural networks in electrical engineering
Chien, E., Li, M., Aportela, A., Ding, K., Jia, S., Maji, S., Zhao, Z., Duarte, J. M., Fung, V., Hao, C., et al. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-024-00076-z · Cited by 29 · see citing works
Acharya, V., Choi, D., Yener, B., & Beamer, G. (2024) · IEEE Access · doi:10.1109/access.2024.3359989 · Cited by 26 · see citing works
Liu, M., Liu, Y., Xu, P., Cui, H., Ke, J., & Ma, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3381994 · Cited by 27 · see citing works
Graph Neural Networks in Computer Vision - Architectures, Datasets and Common Approaches
Krzywda, M., Łukasik, S., & Gandomi, A. H. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892658 · Cited by 25 · see citing works
Jiang, H., Zhang, P., Che, C., Jin, B., & Zhu, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106306 · Cited by 25 · see citing works
Graph pooling in graph neural networks: methods and their applications in omics studies
Wang, Y., Hou, W., Sheng, N., Zhao, Z., Liu, J., Huang, L., & Wang, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10918-9 · Cited by 24 · see citing works
A graph neural network framework for mapping histological topology in oral mucosal tissue
Nair, A. A., Arvidsson, H., V., J. E. G., Tudzarovski, N., Meinke, K., & Sugars, R. V. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05063-5 · Cited by 24 · see citing works
Modak, S., Abdel‐Raheem, E., & Rueda, L. G. (2023) · Biomedical Engineering Advances · doi:10.1016/j.bea.2023.100076 · Cited by 23 · see citing works
Multi-Cell Type and Multi-Level Graph Aggregation Network for Cancer Grading in Pathology Images
Abbas, S. F., Vuong, T. T. L., Kim, K., Song, B., & Kwak, J. T. (2022) · Medical Image Analysis · doi:10.1016/j.media.2023.102936 · Cited by 23 · see citing works
Hybrid Supervision Learning for Pathology Whole Slide Image Classification
Li, J., Chen, W., Huang, X., Yang, S., Hu, Z., Duan, Q., Metaxas, D., Li, H., & Zhang, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_30 · Cited by 22 · see citing works
Ding, S., Gao, Z., Wang, J., Lu, M., & Shi, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118793 · Cited by 22 · see citing works
Hou, W., Huang, H., Qiong, P., Yu, R., Yu, L., & Wang, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_18 · Cited by 22 · see citing works
Structure Embedded Nucleus Classification for Histopathology Images
Lou, W., Wan, X., Li, G., Lou, X., Li, C., Gao, F., & Li, H. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3388328 · Cited by 22 · see citing works
Saranti, A., Pfeifer, B., Gollob, C., Stampfer, K., & Holzinger, A. (2024) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1554 · Cited by 24 · see citing works
Hierarchical Graph Pathomic Network for Progression Free Survival Prediction
Wang, Z., Li, J., Pan, Z., Li, W., Sisk, A. E. J., Ye, H., Speier, W. F., & Arnold, C. W. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_22 · Cited by 21 · see citing works
A Scalable Graph-Based Framework for Multi-Organ Histology Image Classification
Bai, Y., Mi, Y., Su, Y., Zhang, B., Zhang, Z., Wu, J., Huang, H., Xiong, Y., Gong, X., & Wang, W. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3199110 · Cited by 20 · see citing works
Starke, L., Hoppe, A. F., Sartori, A., Stefenon, S. F., Paz, J. F. D., & Leithardt, V. R. Q. (2023) · Scientific Reports · doi:10.1038/s41598-023-43972-4 · Cited by 17 · see citing works
A survey on cell nuclei instance segmentation and classification: Leveraging context and attention
Nunes, J. D., Montezuma, D., Oliveira, D., Pereira, T., & Cardoso, J. S. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103360 · Cited by 18 · see citing works
Deep Multi-Resolution Dictionary Learning for Histopathology Image AnalysisPreprint
Hatami, N., Bilal, M., & Rajpoot, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.00669 · Cited by 15 · see citing works
Self-citationGated Graph Attention Network for Cancer Prediction
Qiu, L., Li, H., Wang, M., & Wang, X. (2021) · Sensors · doi:10.3390/s21061938 · Cited by 14 · see citing works
GQ-GCN: Group Quadratic Graph Convolutional Network for Classification of Histopathological Images
Gao, Z., Shi, J., & Wang, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_12 · Cited by 14 · see citing works
VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology
Nakhli, R., Rich, K., Zhang, A. A., Darbandsari, A., Shenasa, E., Hadjifaradji, A., Thiessen, S., Milne, K., Jones, S. J. M., McAlpine, J. N., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48062-1 · Cited by 14 · see citing works
Ms-Gwnn: Multi-Scale Graph Wavelet Neural Network for Breast Cancer Diagnosis
Zhang, M., Dong, B., & Li, Q. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761464 · Cited by 13 · see citing works
Early Detection of Liver Fibrosis Using Graph Convolutional Networks
Wojciechowska, M., Malacrino, S., Garcia-Martin, N., Fehri, H., & Rittscher, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_21 · Cited by 13 · see citing works
Diagnose Colon and Lung Cancer Histopathological Images Using Pre-Trained Machine Learning Model
Maheshwari, U., Kiranmayee, B. V., & Suresh, C. (2022) · International Conference on Contemporary Computing and Informatics (IC3I) · doi:10.1109/ic3i56241.2022.10073184 · Cited by 12 · see citing works
Cell Graph Transformer for Nuclei Classification
Lou, W., Li, G., Wan, X., & Li, H. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i4.28179 · Cited by 12 · see citing works
Hepatic and portal vein segmentation with dual‐stream deep neural network
Xu, J., Jiang, W., Wu, J., Zhang, W., Zhu, Z., Xin, J., Zheng, N., & Wang, B. (2024) · Medical Physics · doi:10.1002/mp.17090 · Cited by 12 · see citing works
Prognosis prediction based on liver histopathological image via graph deep learning and transformer
Zhang, J., Sun, Z., Wang, K., Wang, C., Cheng, S., Jiang, Y. Y., & Bai, Q. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.111653 · Cited by 12 · see citing works
Wu, R., Chen, Z., Yu, J., Lai, P., Chen, X., Han, A., Xu, M. Q., Fan, Z., Cheng, B., Jiang, Y. L., et al. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05550-8 · Cited by 12 · see citing works
CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images
Baranwal, M., Krishnan, S., Oneka, M. D., Frankel, T. L., & Rao, A. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.727610 · Cited by 12 · see citing works
Artificial intelligence techniques for neuropathological diagnostics and research
Alzoubi, I., Bao, G., Zheng, Y., Wang, X., & Graeber, M. B. (2022) · Neuropathology · doi:10.1111/neup.12880 · Cited by 12 · see citing works
He, Q., Wang, C., Zeng, S., Liang, Z., Duan, H., Yang, J., Pan, F., He, Y., Huang, W., & Guan, T. M. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22952 · Cited by 11 · see citing works
Alzoubi, I., Zhang, L., Zheng, Y., Loh, C., Wang, X., & Graeber, M. B. (2024) · Cancers · doi:10.3390/cancers16040750 · Cited by 11 · see citing works
Tang, L., Diao, S., Li, C., He, M., Ru, K., & Qin, W. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102378 · Cited by 11 · see citing works
Zhao, Y.-S., Li, L.-S., Yu, X., Han, K., Duan, J.-X., Liang, D., Chai, N.-L., & Li, Z.-C. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107607 · Cited by 11 · see citing works
Lee, J. C., Byeon, K., Song, B., Kim, K., & Kwak, J. T. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103708 · Cited by 11 · see citing works
Svecic, A., Mansour, R., Tang, A., & Kadoury, S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259692 · Cited by 11 · see citing works
Instance-level semantic segmentation of nuclei based on multimodal structure encoding
Guan, B., Chu, G., Wang, Z., Li, J., & Yi, B. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06066-8 · Cited by 10 · see citing works
Colorectal Cancer Disease Classification and Segmentation Using A Novel Deep Learning Approach
(2022) · International journal of intelligent engineering and systems · doi:10.22266/ijies2022.0831.21 · Cited by 10 · see citing works
- 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
