# Multiple Instance Learning: A Survey of Problem Characteristics and Applications

arXiv: https://arxiv.org/abs/1612.03365 · HTML: https://mlsanity.com/paper/1612.03365/

## Key facts

- Cited by 164 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2016.

## Built on by (most cited first, 30 of 164)

- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential](https://mlsanity.com/paper/2206.04425/) (2022)
- [Learning to Select Cuts for Efficient Mixed-Integer Programming](https://mlsanity.com/paper/2105.13645/) (2021)
- [On Sparse Modern Hopfield Model](https://mlsanity.com/paper/2309.12673/) (2023)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imaging](https://mlsanity.com/paper/2303.00175/) (2023)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2111.01556/) (2021)
- [Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation](https://mlsanity.com/paper/2101.11223/) (2021)
- [Inductive Relation Prediction by BERT](https://mlsanity.com/paper/2103.07102/) (2021)
- [AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images](https://mlsanity.com/paper/2212.06515/) (2022)
- [Weakly Supervised Anomaly Detection: A Survey](https://mlsanity.com/paper/2302.04549/) (2023)
- [Weakly Supervised Action Selection Learning in Video](https://mlsanity.com/paper/2105.02439/) (2021)
- [Robust Hallucination Detection in LLMs via Adaptive Token Selection](https://mlsanity.com/paper/2504.07863/) (2025)
- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [Nonparametric Modern Hopfield Models](https://mlsanity.com/paper/2404.03900/) (2024)
- [xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology](https://mlsanity.com/paper/2406.04280/) (2024)
- [A Survey of Pathology Foundation Model: Progress and Future Directions](https://mlsanity.com/paper/2504.04045/) (2025)
- [Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning](https://mlsanity.com/paper/2205.15367/) (2022)
- [Movies2Scenes: Using Movie Metadata to Learn Scene Representation](https://mlsanity.com/paper/2202.10650/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Weakly Supervised AUC Optimization: A Unified Partial AUC Approach](https://mlsanity.com/paper/2305.14258/) (2023)
- [Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests](https://mlsanity.com/paper/2310.17867/) (2023)
- [Model Agnostic Interpretability for Multiple Instance Learning](https://mlsanity.com/paper/2201.11701/) (2022)
- [DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis](https://mlsanity.com/paper/2206.05782/) (2022)
- [Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2305.16912/) (2023)
- [Sm: enhanced localization in Multiple Instance Learning for medical imaging classification](https://mlsanity.com/paper/2410.03276/) (2024)
- [MRI-based Alzheimer's disease prediction via distilling the knowledge in multi-modal data](https://mlsanity.com/paper/2104.03618/) (2021)
- [WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images](https://mlsanity.com/paper/2105.10445/) (2021)
- [Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision](https://mlsanity.com/paper/2212.08997/) (2022)

## Abstract (arXiv)

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.