# Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

arXiv: https://arxiv.org/abs/1806.05083 · HTML: https://mlsanity.com/paper/1806.05083/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 17 of 17)

- [Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond](https://mlsanity.com/paper/2102.01998/) (2021)
- [Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides](https://mlsanity.com/paper/2112.02222/) (2021)
- [DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2206.08861/) (2022)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [Holding AI to Account: Challenges for the Delivery of Trustworthy AI in Healthcare](https://mlsanity.com/paper/2211.16444/) (2022)
- [Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction](https://mlsanity.com/paper/2105.14773/) (2021)
- [BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images](https://mlsanity.com/paper/2110.04069/) (2021)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning](https://mlsanity.com/paper/2107.03602/) (2021)
- [Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning](https://mlsanity.com/paper/2302.08867/) (2023)
- [Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review](https://mlsanity.com/paper/2211.14847/) (2022)
- [Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning](https://mlsanity.com/paper/2109.06547/) (2021)
- [Towards Explainable End-to-End Prostate Cancer Relapse Prediction from H&E Images Combining Self-Attention Multiple Instance Learning with a Recurrent Neural Network](https://mlsanity.com/paper/2111.13439/) (2021)
- [Colorectal cancer survival prediction using deep distribution based multiple-instance learning](https://mlsanity.com/paper/2204.11294/) (2022)
- [DepViT-CAD: Deployable Vision Transformer-Based Cancer Diagnosis in Histopathology](https://mlsanity.com/paper/2507.10250/) (2025)
- [Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides](https://mlsanity.com/paper/2304.09314/) (2023)
- [Efficient Whole Slide Image Classification through Fisher Vector Representation](https://mlsanity.com/paper/2411.08530/) (2024)

## Abstract (arXiv)

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity.