# ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

arXiv: https://arxiv.org/abs/2306.10535 · HTML: https://mlsanity.com/paper/2306.10535/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 18 Jun 2023.

## Builds on (most cited first, 18 of 18)

- [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)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [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)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification](https://mlsanity.com/paper/2203.12081/) (2022)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Multiple Instance Learning: A Survey of Problem Characteristics and Applications](https://mlsanity.com/paper/1612.03365/) (2016)
- [Self supervised contrastive learning for digital histopathology](https://mlsanity.com/paper/2011.13971/) (2020)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images](https://mlsanity.com/paper/2001.01599/) (2020)
- [DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2206.08861/) (2022)
- [Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning](https://mlsanity.com/paper/1511.05286/) (2015)
- [Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2111.01556/) (2021)
- [ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification](https://mlsanity.com/paper/2108.10612/) (2021)
- [Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning](https://mlsanity.com/paper/1412.1135/) (2014)
- [Multi-Attention Multiple Instance Learning](https://mlsanity.com/paper/2112.06071/) (2021)

## Built on by (most cited first, 3 of 3)

- [xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology](https://mlsanity.com/paper/2406.04280/) (2024)
- [SGPMIL: Sparse Gaussian Process Multiple Instance Learning](https://mlsanity.com/paper/2507.08711/) (2025)
- [Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classification](https://mlsanity.com/paper/2503.17983/) (2025)

## Abstract (arXiv)

Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available.