# A Two-Stage Multiple Instance Learning Framework for the Detection of Breast Cancer in Mammograms

arXiv: https://arxiv.org/abs/2004.11726 · HTML: https://mlsanity.com/paper/2004.11726/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

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

- [M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector](https://mlsanity.com/paper/2308.06420/) (2023)
- [Computer-Aided Cancer Diagnosis via Machine Learning and Deep Learning: A comparative review](https://mlsanity.com/paper/2210.11943/) (2022)
- [A Contrastive Learning Framework for Breast Cancer Detection](https://mlsanity.com/paper/2509.20474/) (2025)

## Abstract (arXiv)

Mammograms are commonly employed in the large scale screening of breast cancer which is primarily characterized by the presence of malignant masses. However, automated image-level detection of malignancy is a challenging task given the small size of the mass regions and difficulty in discriminating between malignant, benign mass and healthy dense fibro-glandular tissue. To address these issues, we explore a two-stage Multiple Instance Learning (MIL) framework. A Convolutional Neural Network (CNN) is trained in the first stage to extract local candidate patches in the mammograms that may contain either a benign or malignant mass. The second stage employs a MIL strategy for an image level benign vs. malignant classification. A global image-level feature is computed as a weighted average of patch-level features learned using a CNN. Our method performed well on the task of localization of masses with an average Precision/Recall of 0.76/0.80 and acheived an average AUC of 0.91 on the imagelevel classification task using a five-fold cross-validation on the INbreast dataset. Restricting the MIL only to the candidate patches extracted in Stage 1 led to a significant improvement in classification performance in comparison to a dense extraction of patches from the entire mammogram.