mlsanityData to 14 Oct 2026

ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

arXiv 2306.10535 ↗First posted 18 Jun 2023eess.IV cs.CV cs.LGcited by 3

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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.

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

18 of 52 references.

References · 18

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  4. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
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  6. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  7. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  8. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  9. 2016Multiple Instance Learning: A Survey of Problem Characteristics and Applicationscited by 164
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  11. 2016Revisiting Multiple Instance Neural Networkscited by 119
  12. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
6 more
  1. 2022DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 40
  2. 2015Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learningcited by 38
  3. 2021Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imagingcited by 31
  4. 2021ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classificationcited by 22
  5. 2014Detector Discovery in the Wild: Joint Multiple Instance and Representation Learningcited by 6
  6. 2021Multi-Attention Multiple Instance Learningcited by 6

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024xMIL: Insightful Explanations for Multiple Instance Learning in Histopathologycited by 16
  2. 2025SGPMIL: Sparse Gaussian Process Multiple Instance Learningcited by 2
  3. 2025Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classificationcited by 0