# Multiple instance learning with graph neural networks

arXiv: https://arxiv.org/abs/1906.04881 · HTML: https://mlsanity.com/paper/1906.04881/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 25 of 25)

- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysis](https://mlsanity.com/paper/2208.08789/) (2022)
- [ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification](https://mlsanity.com/paper/2108.10612/) (2021)
- [Entity-centered Cross-document Relation Extraction](https://mlsanity.com/paper/2210.16541/) (2022)
- [Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning](https://mlsanity.com/paper/2205.15367/) (2022)
- [CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images](https://mlsanity.com/paper/2305.05314/) (2023)
- [Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests](https://mlsanity.com/paper/2310.17867/) (2023)
- [SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2105.02726/) (2021)
- [Model Agnostic Interpretability for Multiple Instance Learning](https://mlsanity.com/paper/2201.11701/) (2022)
- [How Effective Can Dropout Be in Multiple Instance Learning ?](https://mlsanity.com/paper/2504.14783/) (2025)
- [Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks](https://mlsanity.com/paper/2202.11132/) (2022)
- [SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2311.00048/) (2023)
- [HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classification](https://mlsanity.com/paper/2103.09764/) (2021)
- [Multi-Instance Learning by Utilizing Structural Relationship among Instances](https://mlsanity.com/paper/2102.01889/) (2021)
- [Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers](https://mlsanity.com/paper/2305.06963/) (2023)
- [Deep Multiple Instance Learning with Distance-Aware Self-Attention](https://mlsanity.com/paper/2305.10552/) (2023)
- [Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classification](https://mlsanity.com/paper/2307.07482/) (2023)
- [Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping](https://mlsanity.com/paper/2407.18105/) (2024)
- [Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training](https://mlsanity.com/paper/2408.04813/) (2024)
- [Text Classification using Graph Convolutional Networks: A Comprehensive Survey](https://mlsanity.com/paper/2410.09399/) (2024)
- [Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios](https://mlsanity.com/paper/2307.14025/) (2023)
- [Weakly Supervised Vulnerability Localization via Multiple Instance Learning](https://mlsanity.com/paper/2509.11312/) (2025)
- [A Vector Symbolic Approach to Multiple Instance Learning](https://mlsanity.com/paper/2511.16795/) (2025)
- [Multi-Label Proportion Learning for Sea-Ice Type Prediction](https://mlsanity.com/paper/2609.16347/) (2026)

## Abstract (arXiv)

Multiple instance learning (MIL) aims to learn the mapping between a bag of instances and the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN) based algorithm for MIL: we treat each bag as a graph and use GNN to learn the bag embedding, in order to explore the useful structural information among instances in bags. The final graph representation is fed into a classifier for label prediction. Our algorithm is the first attempt to use GNN for MIL. We empirically show that the proposed algorithm achieves the state of the art performance on several popular MIL data sets without losing model interpretability.