Representation Learning of Histopathology Images using Graph Neural Networks
Open accessAbstract
Representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage framework for WSI representation learning. We sample relevant patches using a color-based method and use graph neural networks to learn relations among sampled patches to aggregate the image information into a single vector representation. We introduce attention via graph pooling to automatically infer patches with higher relevance. We demonstrate the performance of our approach for discriminating two sub-types of lung cancers, Lung Adenocarcinoma (LUAD) & Lung Squamous Cell Carcinoma (LUSC). We collected 1,026 lung cancer WSIs with the 40× magnification from The Cancer Genome Atlas (TCGA) dataset, the largest public repository of histopathology images and achieved state-of-the-art accuracy of 88.8% and AUC of 0.89 on lung cancer sub-type classification by extracting features from a pre-trained DenseNet model.
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Adnan et al. demonstrated an application of graph convolution neural networks to MIL in digital pathology and achieved state of the art accuracy on a lung sub cancer classification task.
“Adnan et al. 32 demonstrated an application of graph convolution neural networks to MIL in digital pathology and achieved state of the art accuracy on a lung sub cancer classification task.”
From Federated Learning and Differential Privacy for Medical Image Analysis (2021) · cites this paper as [32]
Fusing compound sequence, structure, and clinical implications has significantly improved drug-drug and drug-target interaction predictions.
“Fusing compound sequence, structure, and clinical implications has significantly improved drug-drug and drug-target interaction predictions.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [176]
Protein structure representations generated by graph convolutions combined with protein sequence representations can predict the probability of compound-protein interactions.
“To be flexible with other graph- and non-graph-based methods, protein structure representations generated by graph convolutions has been combined with protein sequence representations (e.g., shallow network embedding methods or CNNs) to predict the probability of compound-protein interactions [174, 175, 176, 177].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [176]
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References 36 · Cited by 109
Research works printed but not linked: 3
- Michaël Defferrard, Xavier Bresson, and Pierre Van- dergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural infor- mation processing systems, pages 3844-3852, 2016. 3
- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kil- ian Q Weinberger. Densely connected convolutional net- works. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708, 2017. 3, 5, 6
- Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barn- abas Poczos, Russ R Salakhutdinov, and Alexander J Smola. Deep sets. In Advances in neural information processing systems, pages 3391-3401, 2017. 3, 4
- Authors
- Adnan, M., Kalra, S., & Tizhoosh, H. R.
- Affiliations
- University of Waterloo (CA)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 4254-4261
- OpenAlex
- W3016852577
- Cited by
- 109
- References
- 36
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 19 · 2023: 26 · 2022: 22 · 2021: 9 · 2020: 2 · 2019: 1
- FWCI
- 5.63
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- whole slide images, histopathological images, representation learning, graph neural networks, graph pooling, attention, patch sampling, lung cancer subtypes, lung adenocarcinoma, lung squamous cell carcinoma, cancer subtyping, TCGA
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