# Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach

arXiv: https://arxiv.org/abs/1912.11027 · HTML: https://mlsanity.com/paper/1912.11027/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 27 of 27)

- [CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios](https://mlsanity.com/paper/2404.15272/) (2024)
- [Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning](https://mlsanity.com/paper/2110.00394/) (2021)
- [Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning](https://mlsanity.com/paper/2111.10827/) (2021)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screening](https://mlsanity.com/paper/2308.01057/) (2023)
- [Multi-task fusion for improving mammography screening data classification](https://mlsanity.com/paper/2112.01320/) (2021)
- [Domain Generalization for Mammographic Image Analysis with Contrastive Learning](https://mlsanity.com/paper/2304.10226/) (2023)
- [Adversarially Robust Feature Learning for Breast Cancer Diagnosis](https://mlsanity.com/paper/2402.08768/) (2024)
- [M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector](https://mlsanity.com/paper/2308.06420/) (2023)
- [CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in Tomosynthesis](https://mlsanity.com/paper/2407.17620/) (2024)
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- [BTMuda: A Bi-level Multi-source unsupervised domain adaptation framework for breast cancer diagnosis](https://mlsanity.com/paper/2408.17054/) (2024)
- [Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and Convolutional Neural Network](https://mlsanity.com/paper/2504.19888/) (2025)
- [LUMINA: A Multi-Vendor Mammography Benchmark with Energy Harmonization Protocol](https://mlsanity.com/paper/2603.14644/) (2026)
- [DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows](https://mlsanity.com/paper/2105.14638/) (2021)
- [A workflow for segmenting soil and plant X-ray CT images with deep learning in Googles Colaboratory](https://mlsanity.com/paper/2203.09674/) (2022)
- [Preparing data for pathological artificial intelligence with clinical-grade performance](https://mlsanity.com/paper/2205.10748/) (2022)
- [Independent evaluation of state-of-the-art deep networks for mammography](https://mlsanity.com/paper/2206.12407/) (2022)
- [An efficient deep neural network to find small objects in large 3D images](https://mlsanity.com/paper/2210.08645/) (2022)
- [Computer-Aided Cancer Diagnosis via Machine Learning and Deep Learning: A comparative review](https://mlsanity.com/paper/2210.11943/) (2022)
- [Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge](https://mlsanity.com/paper/2212.12303/) (2022)
- [Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples](https://mlsanity.com/paper/2307.14907/) (2023)
- [Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection](https://mlsanity.com/paper/2312.00067/) (2023)
- [Revisiting Invariant Learning for Out-of-Domain Generalization on Multi-Site Mammogram Datasets](https://mlsanity.com/paper/2503.06759/) (2025)
- [Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets](https://mlsanity.com/paper/2607.06549/) (2026)
- [M3D-Net: Hierarchical Coordination of Spatial Context, Feature Reuse, and Differential Attention for Mammography Classification](https://mlsanity.com/paper/2609.27523/) (2026)

## Abstract (arXiv)

Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018. To achieve earlier breast cancer detection, screening x-ray mammography is recommended by health organizations worldwide and has been estimated to decrease breast cancer mortality by 20-40%. Nevertheless, significant false positive and false negative rates, as well as high interpretation costs, leave opportunities for improving quality and access. To address these limitations, there has been much recent interest in applying deep learning to mammography; however, obtaining large amounts of annotated data poses a challenge for training deep learning models for this purpose, as does ensuring generalization beyond the populations represented in the training dataset. Here, we present an annotation-efficient deep learning approach that 1) achieves state-of-the-art performance in mammogram classification, 2) successfully extends to digital breast tomosynthesis (DBT; "3D mammography"), 3) detects cancers in clinically-negative prior mammograms of cancer patients, 4) generalizes well to a population with low screening rates, and 5) outperforms five-out-of-five full-time breast imaging specialists by improving absolute sensitivity by an average of 14%. Our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.