A Graph-Transformer for Whole Slide Image Classification
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Abstract
Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading. Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade. We selected 4,818 WSIs from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the National Lung Screening Trial (NLST), and The Cancer Genome Atlas (TCGA), and used GTP to distinguish adenocarcinoma (LUAD) and squamous cell carcinoma (LSCC) from adjacent non-cancerous tissue (normal). First, using NLST data, we developed a contrastive learning framework to generate a feature extractor. This allowed us to compute feature vectors of individual WSI patches, which were used to represent the nodes of the graph followed by construction of the GTP framework. Our model trained on the CPTAC data achieved consistently high performance on three-label classification (normal versus LUAD versus LSCC: mean accuracy = 91.2 ± 2.5%) based on five-fold cross-validation, and mean accuracy = 82.3 ± 1.0% on external test data (TCGA). We also introduced a graph-based saliency mapping technique, called GraphCAM, that can identify regions that are highly associated with the class label. Our findings demonstrate GTP as an interpretable and effective deep learning framework for WSI-level classification.
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The work is unique in mixing node and edge embeddings along with fusion of bulk gene expression embeddings to learn a multimodal topographic mapping to predict survival.
“While other researchers have previously explored the development of mid-level-fusion and mixer architectures [6], [7] as well as the use of graph-based representations of WSIs [8], our work is unique in mixing node and edge embeddings along with fusion of bulk gene expression embeddings to learn a multimodal topographic mapping to predict survival.”
From Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival (2024) · cites this paper as [8]
The model was trained on TCGA using 5-fold cross-validation and tested on CPTAC as an independent dataset.
“We used NLST for generating node-level features for graph construction [8], TCGA for training the models using 5-fold cross-validation, and CPTAC as an independent dataset for testing.”
From Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival (2024) · cites this paper as [8]
The ISM model's performance exceeded that of other recent methodologies [8], [21], [25]-[27].
“The ISM model’s performance exceeded that of other recent methodologies [8], [21], [25]-[27], as illustrated in Table II.”
From Graph attention-based fusion of pathology images and gene expression for prediction of cancer survival (2024) · cites this paper as [8]
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Cell Graph Transformer for Nuclei Classification
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- Authors
- Zheng, Y., Gindra, R. H., Green, E. J., Burks, E. J., Betke, M., Beane, J. E., & Kolachalama, V. B.
- Affiliations
- Boston University (US)
- ORCID
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2022-11
- Volume
- 41
- Issue
- 11
- Pages
- 3003-3015
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- PMC
- PMC9670036
- OpenAlex
- W4281257868
- Cited by
- 255
- References
- 47
- Citations by year
- 2026: 54 · 2025: 81 · 2024: 73 · 2023: 42 · 2022: 5
- FWCI
- 16.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Grants from the National Institutes of Heath (R21-CA253498, R01-HL159620)
- Johnson & Johnson Enterprise Innovation, Inc.
- American Heart Association (20SFRN35460031)
- Karen Toffler Charitable Trust
- National Science Foundation (1551572, 1838193)
- 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 › Advanced Neural Network Applications
- Keywords
- whole slide image classification, graph transformer, vision transformer, lung adenocarcinoma, squamous cell carcinoma, lung cancer histopathology, contrastive learning, interpretability
- MeSH terms
- Guanosine Triphosphate, Image Processing, Computer-Assisted, Proteomics
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