ArticleIEEE transactions on medical imaging2022
A Graph-Transformer for Whole Slide Image Classification.
Article in IEEE transactions on medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- CSNet: Cognitive Simulation Network for Reliable Histopathology-Based Cancer Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Deep learning-powered quantification of endocrine cells and CD3Diabetologia · 2026Article
- Deep learning based histomorphological phenotyping and prognostic stratification for combined SCLC and LCNEC.NPJ digital medicine · 2026Article
- Pathology-prior driven substructure-aware graph neural network for whole slide image classification.Scientific reports · 2026Article
- CLWD: a Chinese histopathology dataset for lung adenocarcinoma subtype classification.Scientific data · 2026Article
- FourierMIL: Fourier Filtering-based Multiple Instance Learning for Whole Slide Image Analysis.International journal of computer vision · 2026Article
- A histopathology image-based computer-aided classification study for oral squamous cell carcinoma.Frontiers in oncology · 2026Article
- Histo-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma.PLoS computational biology · 2026Article
- Encoding functional edges in graphs to model spatially varying relationships in the tumor microenvironment.NPJ artificial intelligence · 2026Article
- Dual-stage deep learning framework for neuroblastoma differentiation by integrating cell segmentation and multiscale modeling.World journal of pediatric surgery · 2026Article
- BPMambaMIL: A bio-inspired prototype-guided multiple instance learning for oncotype DX risk assessment in histopathology.Computer methods and programs in biomedicine · 2025Article
- Machine Learning Models Using General and Tissue-Specific Feature Extractors for Accurate Subtyping of Biopsy Samples: Advancing Lung Cancer Diagnosis in Latin America.JTO clinical and research reports · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- TransBreastNet a CNN transformer hybrid deep learning framework for breast cancer subtype classification and temporal lesion progression analysis.Scientific reports · 2025Article
- Deep Learning Image Processing Models in Dermatopathology.Diagnostics (Basel, Switzerland) · 2025Review
- Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.Mayo Clinic proceedings. Digital health · 2025Review
- Pulmonary diseases accurate recognition using adaptive multiscale feature fusion in chest radiography.Scientific reports · 2025Article
- Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks.BMC medical imaging · 2025Article
- Generating dermatopathology reports from gigapixel whole slide images with HistoGPT.Nature communications · 2025Article
- Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images.Scientific reports · 2025Article
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7 authors.
Funding
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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