mlsanityData to 7 Oct 2026

Multiple instance learning with graph neural networks

arXiv 1906.04881 ↗First posted 12 Jun 2019cs.LG stat.MLcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 6; 2023: 7; 2024: 3; 2025: 3; 2026: 1.

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  1. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  2. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
  3. 2022Towards 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 Analysiscited by 29
  4. 2021ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classificationcited by 22
  5. 2022Entity-centered Cross-document Relation Extractioncited by 18
  6. 2022Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learningcited by 13
  7. 2023CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Imagescited by 13
  8. 2023Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Testscited by 11
  9. 2021SparseConvMIL: Sparse Convolutional Context-Aware Multiple Instance Learning for Whole Slide Image Classificationcited by 10
  10. 2022Model Agnostic Interpretability for Multiple Instance Learningcited by 10
  11. 2025How Effective Can Dropout Be in Multiple Instance Learning ?cited by 7
  12. 2022Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networkscited by 6
13 more
  1. 2023SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classificationcited by 6
  2. 2021HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classificationcited by 3
  3. 2021Multi-Instance Learning by Utilizing Structural Relationship among Instancescited by 1
  4. 2023Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformerscited by 1
  5. 2023Deep Multiple Instance Learning with Distance-Aware Self-Attentioncited by 1
  6. 2023Dual-Query Multiple Instance Learning for Dynamic Meta-Embedding based Tumor Classificationcited by 1
  7. 2024Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtypingcited by 1
  8. 2024Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Trainingcited by 1
  9. 2024Text Classification using Graph Convolutional Networks: A Comprehensive Surveycited by 1
  10. 2023Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarioscited by 0
  11. 2025Weakly Supervised Vulnerability Localization via Multiple Instance Learningcited by 0
  12. 2025A Vector Symbolic Approach to Multiple Instance Learningcited by 0
  13. 2026Multi-Label Proportion Learning for Sea-Ice Type Predictioncited by 0