# Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model

arXiv: https://arxiv.org/abs/2011.07995 · HTML: https://mlsanity.com/paper/2011.07995/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

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

- [A Systematic Collection of Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2106.12864/) (2021)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Convolutional Neural Networks Rarely Learn Shape for Semantic Segmentation](https://mlsanity.com/paper/2305.06568/) (2023)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion](https://mlsanity.com/paper/2305.03098/) (2023)
- [SIFT-DBT: Self-supervised Initialization and Fine-Tuning for Imbalanced Digital Breast Tomosynthesis Image Classification](https://mlsanity.com/paper/2403.13148/) (2024)
- [CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in Tomosynthesis](https://mlsanity.com/paper/2407.17620/) (2024)
- [Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data](https://mlsanity.com/paper/2501.19094/) (2025)
- [A survey on deep learning approaches for breast cancer diagnosis](https://mlsanity.com/paper/2109.08853/) (2021)
- [A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study](https://mlsanity.com/paper/2504.05636/) (2025)
- [T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images](https://mlsanity.com/paper/2507.04038/) (2025)
- [MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonization](https://mlsanity.com/paper/2511.02400/) (2025)
- [Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift](https://mlsanity.com/paper/2607.10358/) (2026)
- [An efficient deep neural network to find small objects in large 3D images](https://mlsanity.com/paper/2210.08645/) (2022)
- [Multi-Head Feature Pyramid Networks for Breast Mass Detection](https://mlsanity.com/paper/2302.11106/) (2023)
- [From 2D to 3D Without Extra Baggage: Data-Efficient Cancer Detection in Digital Breast Tomosynthesis](https://mlsanity.com/paper/2511.10597/) (2025)
- [DBT-DINO: Towards Foundation model based analysis of Digital Breast Tomosynthesis](https://mlsanity.com/paper/2512.13608/) (2025)

## Abstract (arXiv)

Breast cancer screening is one of the most common radiological tasks with over 39 million exams performed each year. While breast cancer screening has been one of the most studied medical imaging applications of artificial intelligence, the development and evaluation of the algorithms are hindered due to the lack of well-annotated large-scale publicly available datasets. This is particularly an issue for digital breast tomosynthesis (DBT) which is a relatively new breast cancer screening modality. We have curated and made publicly available a large-scale dataset of digital breast tomosynthesis images. It contains 22,032 reconstructed DBT volumes belonging to 5,610 studies from 5,060 patients. This included four groups: (1) 5,129 normal studies, (2) 280 studies where additional imaging was needed but no biopsy was performed, (3) 112 benign biopsied studies, and (4) 89 studies with cancer. Our dataset included masses and architectural distortions which were annotated by two experienced radiologists. Additionally, we developed a single-phase deep learning detection model and tested it using our dataset to serve as a baseline for future research. Our model reached a sensitivity of 65% at 2 false positives per breast. Our large, diverse, and highly-curated dataset will facilitate development and evaluation of AI algorithms for breast cancer screening through providing data for training as well as common set of cases for model validation. The performance of the model developed in our study shows that the task remains challenging and will serve as a baseline for future model development.