# Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net

arXiv: https://arxiv.org/abs/1904.12319 · HTML: https://mlsanity.com/paper/1904.12319/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Robust and Efficient Medical Imaging with Self-Supervision](https://mlsanity.com/paper/2205.09723/) (2022)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods](https://mlsanity.com/paper/2308.03486/) (2023)
- [Auxiliary Input in Training: Incorporating Catheter Features into Deep Learning Models for ECG-Free Dynamic Coronary Roadmapping](https://mlsanity.com/paper/2408.15947/) (2024)
- [DoSReMC: Domain Shift Resilient Mammography Classification using Batch Normalization Adaptation](https://mlsanity.com/paper/2508.15452/) (2025)
- [Evaluating Federated Learning approaches for mammography under breast density heterogeneity](https://mlsanity.com/paper/2605.09137/) (2026)

## Abstract (arXiv)

The high cost of generating expert annotations, poses a strong limitation for supervised machine learning methods in medical imaging. Weakly supervised methods may provide a solution to this tangle. In this study, we propose a novel deep learning architecture for multi-class classification of mammograms according to the severity of their containing anomalies, having only a global tag over the image. The suggested scheme further allows localization of the different types of findings in full resolution. The new scheme contains a dual branch network that combines region-level classification with region ranking. We evaluate our method on a large multi-center mammography dataset including ~3,000 mammograms with various anomalies and demonstrate the advantages of the proposed method over a previous weakly-supervised strategy.