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

arXiv: https://arxiv.org/abs/2110.06042 · HTML: https://mlsanity.com/paper/2110.06042/

## 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 (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images](https://mlsanity.com/paper/1812.06499/) (2018)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [Machine Learning on Graphs: A Model and Comprehensive Taxonomy](https://mlsanity.com/paper/2005.03675/) (2020)
- [HistoCartography: A Toolkit for Graph Analytics in Digital Pathology](https://mlsanity.com/paper/2107.10073/) (2021)
- [Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues](https://mlsanity.com/paper/1705.08369/) (2017)
- [ALBRT: Cellular Composition Prediction in Routine Histology Images](https://mlsanity.com/paper/2108.08306/) (2021)
- [Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography](https://mlsanity.com/paper/1901.01969/) (2019)
- [A new nonlocal forward model for diffuse optical tomography](https://mlsanity.com/paper/1906.00882/) (2019)

## Built on by (most cited first, 30 of 31)

- [WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering](https://mlsanity.com/paper/2407.05603/) (2024)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [An Aggregation of Aggregation Methods in Computational Pathology](https://mlsanity.com/paper/2211.01256/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis](https://mlsanity.com/paper/2411.18101/) (2024)
- [Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks](https://mlsanity.com/paper/2407.08625/) (2024)
- [NCT-CRC-HE: Not All Histopathological Datasets Are Equally Useful](https://mlsanity.com/paper/2409.11546/) (2024)
- [Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification](https://mlsanity.com/paper/2305.02032/) (2023)
- [A Fully Automated and Explainable Algorithm for the Prediction of Malignant Transformation in Oral Epithelial Dysplasia](https://mlsanity.com/paper/2307.03757/) (2023)
- [CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained images](https://mlsanity.com/paper/2401.16131/) (2024)
- [CellOMaps: A Compact Representation for Robust Classification of Lung Adenocarcinoma Growth Patterns](https://mlsanity.com/paper/2501.08094/) (2025)
- [A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs](https://mlsanity.com/paper/2211.06292/) (2022)
- [convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer](https://mlsanity.com/paper/2211.10690/) (2022)
- [Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review](https://mlsanity.com/paper/2211.14847/) (2022)
- [SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification](https://mlsanity.com/paper/2407.08968/) (2024)
- [Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2410.10260/) (2024)
- [Multi-modal Knowledge Decomposition based Online Distillation for Biomarker Prediction in Breast Cancer Histopathology](https://mlsanity.com/paper/2508.17213/) (2025)
- [Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling](https://mlsanity.com/paper/2512.17326/) (2025)
- [How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images](https://mlsanity.com/paper/2206.07599/) (2022)
- [Language models are good pathologists: using attention-based sequence reduction and text-pretrained transformers for efficient WSI classification](https://mlsanity.com/paper/2211.07384/) (2022)
- [Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide Images](https://mlsanity.com/paper/2301.09624/) (2023)
- [Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks](https://mlsanity.com/paper/2403.15068/) (2024)
- [SlideMamba: Entropy-Based Adaptive Fusion of GNN and Mamba for Enhanced Representation Learning in Digital Pathology](https://mlsanity.com/paper/2509.21239/) (2025)
- [MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Images](https://mlsanity.com/paper/2302.12653/) (2023)
- [NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer](https://mlsanity.com/paper/2411.09766/) (2024)
- [SEW: Self-calibration Enhanced Whole Slide Pathology Image Analysis](https://mlsanity.com/paper/2412.10853/) (2024)
- [A Novel Approach to Linking Histology Images with DNA Methylation](https://mlsanity.com/paper/2504.05403/) (2025)
- [GNCAF: A GNN-based Neighboring Context Aggregation Framework for Tertiary Lymphoid Structures Semantic Segmentation in WSI](https://mlsanity.com/paper/2505.08430/) (2025)
- [Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping](https://mlsanity.com/paper/2509.03408/) (2025)
- [A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection](https://mlsanity.com/paper/2604.01798/) (2026)

## 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.