# Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2107.13048 · HTML: https://mlsanity.com/paper/2107.13048/

## Key facts

- Cited by 124 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images](https://mlsanity.com/paper/2004.09666/) (2020)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary](https://mlsanity.com/paper/2006.13932/) (2020)
- [CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images](https://mlsanity.com/paper/1909.01068/) (2019)
- [Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images](https://mlsanity.com/paper/1810.00236/) (2018)
- [Federated Learning for Computational Pathology on Gigapixel Whole Slide Images](https://mlsanity.com/paper/2009.10190/) (2020)
- [Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding](https://mlsanity.com/paper/1910.10825/) (2019)
- [Quantifying Explainers of Graph Neural Networks in Computational Pathology](https://mlsanity.com/paper/2011.12646/) (2020)
- [Interpretable Survival Prediction for Colorectal Cancer using Deep Learning](https://mlsanity.com/paper/2011.08965/) (2020)
- [Histographs: Graphs in Histopathology](https://mlsanity.com/paper/1908.05020/) (2019)
- [Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks](https://mlsanity.com/paper/1910.13328/) (2019)
- [A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma](https://mlsanity.com/paper/2104.12862/) (2021)

## Built on by (most cited first, 30 of 124)

- [Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images](https://mlsanity.com/paper/2306.07831/) (2023)
- [Artificial Intelligence for Digital and Computational Pathology](https://mlsanity.com/paper/2401.06148/) (2023)
- [A graph-transformer for whole slide image classification](https://mlsanity.com/paper/2205.09671/) (2022)
- [Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2403.07719/) (2024)
- [Self-Supervised Vision Transformers Learn Visual Concepts in Histopathology](https://mlsanity.com/paper/2203.00585/) (2022)
- [Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning](https://mlsanity.com/paper/2307.04189/) (2023)
- [AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images](https://mlsanity.com/paper/2212.06515/) (2022)
- [AMIGO: Sparse Multi-Modal Graph Transformer with Shared-Context Processing for Representation Learning of Giga-pixel Images](https://mlsanity.com/paper/2303.00865/) (2023)
- [HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis](https://mlsanity.com/paper/2309.07400/) (2023)
- [MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning](https://mlsanity.com/paper/2408.11505/) (2024)
- [HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image](https://mlsanity.com/paper/2306.17373/) (2023)
- [Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology](https://mlsanity.com/paper/2409.09369/) (2024)
- [MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models](https://mlsanity.com/paper/2403.05160/) (2024)
- [Rethinking Transformer for Long Contextual Histopathology Whole Slide Image Analysis](https://mlsanity.com/paper/2410.14195/) (2024)
- [Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling](https://mlsanity.com/paper/2206.08885/) (2022)
- [Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance](https://mlsanity.com/paper/2505.11997/) (2025)
- [DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis](https://mlsanity.com/paper/2206.05782/) (2022)
- [Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective](https://mlsanity.com/paper/2407.07841/) (2024)
- [Sm: enhanced localization in Multiple Instance Learning for medical imaging classification](https://mlsanity.com/paper/2410.03276/) (2024)
- [MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features](https://mlsanity.com/paper/2311.11659/) (2023)
- [SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images](https://mlsanity.com/paper/2407.00664/) (2024)
- [Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications](https://mlsanity.com/paper/2202.08916/) (2022)
- [Kernel Attention Transformer (KAT) for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2206.13156/) (2022)
- [SurvMamba: State Space Model with Multi-grained Multi-modal Interaction for Survival Prediction](https://mlsanity.com/paper/2404.08027/) (2024)
- [Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images](https://mlsanity.com/paper/2406.04377/) (2024)
- [GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation](https://mlsanity.com/paper/2407.05540/) (2024)
- [From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification](https://mlsanity.com/paper/2408.09449/) (2024)
- [MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis](https://mlsanity.com/paper/2506.18028/) (2025)
- [Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images](https://mlsanity.com/paper/2212.08781/) (2022)
- [Histo-Genomic Knowledge Distillation For Cancer Prognosis From Histopathology Whole Slide Images](https://mlsanity.com/paper/2403.10040/) (2024)

## Abstract (arXiv)

Cancer prognostication is a challenging task in computational pathology that requires context-aware representations of histology features to adequately infer patient survival. Despite the advancements made in weakly-supervised deep learning, many approaches are not context-aware and are unable to model important morphological feature interactions between cell identities and tissue types that are prognostic for patient survival. In this work, we present Patch-GCN, a context-aware, spatially-resolved patch-based graph convolutional network that hierarchically aggregates instance-level histology features to model local- and global-level topological structures in the tumor microenvironment. We validate Patch-GCN with 4,370 gigapixel WSIs across five different cancer types from the Cancer Genome Atlas (TCGA), and demonstrate that Patch-GCN outperforms all prior weakly-supervised approaches by 3.58-9.46%. Our code and corresponding models are publicly available at https://github.com/mahmoodlab/Patch-GCN.