mlsanityData to 14 Oct 2026

SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer

arXiv 2110.06042 ↗First posted 12 Oct 2021cs.CV cs.LGcited by 31

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Abstract (arXiv)

Human epidermal growth factor receptor 2 (HER2) is an important prognostic and predictive factor which is overexpressed in 15-20% of breast cancer (BCa). The determination of its status is a key clinical decision making step for selection of treatment regimen and prognostication. HER2 status is evaluated using transcroptomics or immunohistochemistry (IHC) through situ hybridisation (ISH) which require additional costs and tissue burden in addition to analytical variabilities in terms of manual observational biases in scoring. In this study, we propose a novel graph neural network (GNN) based model (termed SlideGraph+) to predict HER2 status directly from whole-slide images of routine Haematoxylin and Eosin (H&E) slides. The network was trained and tested on slides from The Cancer Genome Atlas (TCGA) in addition to two independent test datasets. We demonstrate that the proposed model outperforms the state-of-the-art methods with area under the ROC curve (AUC) values > 0.75 on TCGA and 0.8 on independent test sets. Our experiments show that the proposed approach can be utilised for case triaging as well as pre-ordering diagnostic tests in a diagnostic setting. It can also be used for other weakly supervised prediction problems in computational pathology. The SlideGraph+ code is available at https://github.com/wenqi006/SlideGraph.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 12 Oct 2021.

Builds on

15 of 61 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  6. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  7. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  8. 2018HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Imagescited by 388
  9. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  10. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  11. 2021HistoCartography: A Toolkit for Graph Analytics in Digital Pathologycited by 20
  12. 2017Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissuescited by 16
3 more
  1. 2021ALBRT: Cellular Composition Prediction in Routine Histology Imagescited by 8
  2. 2019Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomographycited by 1
  3. 2019A new nonlocal forward model for diffuse optical tomographycited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 6; 2023: 6; 2024: 10; 2025: 7; 2026: 2.

31 papers.

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  1. 2024WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answeringcited by 34
  2. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  3. 2022An Aggregation of Aggregation Methods in Computational Pathologycited by 20
  4. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 11
  5. 2024Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysiscited by 10
  6. 2024Histopathological Image Classification with Cell Morphology Aware Deep Neural Networkscited by 5
  7. 2024NCT-CRC-HE: Not All Histopathological Datasets Are Equally Usefulcited by 5
  8. 2023Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classificationcited by 3
  9. 2023A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasiacited by 3
  10. 2024CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained imagescited by 3
  11. 2025CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patternscited by 3
  12. 2022A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphscited by 2
19 more
  1. 2022convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancercited by 2
  2. 2022Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Reviewcited by 2
  3. 2024SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classificationcited by 2
  4. 2024Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysiscited by 2
  5. 2025Multi-modal Knowledge Decomposition based Online Distillation for Biomarker Prediction in Breast Cancer Histopathologycited by 2
  6. 2025Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modellingcited by 2
  7. 2022How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Imagescited by 1
  8. 2022Language models are good pathologists: using attention-based sequence reduction and text-pretrained transformers for efficient WSI classificationcited by 1
  9. 2023Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide Imagescited by 1
  10. 2024Integrating multiscale topology in digital pathology with pyramidal graph convolutional networkscited by 1
  11. 2025SlideMamba: Entropy-Based Adaptive Fusion of GNN and Mamba for Enhanced Representation Learning in Digital Pathologycited by 1
  12. 2023MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Imagescited by 0
  13. 2024NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancercited by 0
  14. 2024SEW: Self-calibration Enhanced Whole Slide Pathology Image Analysiscited by 0
  15. 2025A Novel Approach to Linking Histology Images with DNA Methylationcited by 0
  16. 2025GNCAF: A GNN-based Neighboring Context Aggregation Framework for Tertiary Lymphoid Structures Semantic Segmentation in WSIcited by 0
  17. 2025Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtypingcited by 0
  18. 2026A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selectioncited by 0
  19. 2026ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysiscited by 0