# High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection

arXiv: https://arxiv.org/abs/2209.09809 · HTML: https://mlsanity.com/paper/2209.09809/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 20 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Distribution Matching Losses Can Hallucinate Features in Medical Image Translation](https://mlsanity.com/paper/1805.08841/) (2018)
- [OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data](https://mlsanity.com/paper/2004.04742/) (2020)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks](https://mlsanity.com/paper/1807.03401/) (2018)
- [Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification](https://mlsanity.com/paper/2203.04961/) (2022)
- [Quality analysis of DCGAN-generated mammography lesions](https://mlsanity.com/paper/1911.12850/) (2019)
- [MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education](https://mlsanity.com/paper/2010.05177/) (2020)
- [Adversarial Augmentation for Enhancing Classification of Mammography Images](https://mlsanity.com/paper/1902.07762/) (2019)

## Built on by (most cited first, 11 of 11)

- [FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare](https://mlsanity.com/paper/2309.12325/) (2023)
- [Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges](https://mlsanity.com/paper/2407.00116/) (2024)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [medigan: a Python library of pretrained generative models for medical image synthesis](https://mlsanity.com/paper/2209.14472/) (2022)
- [Robust image representations with counterfactual contrastive learning](https://mlsanity.com/paper/2409.10365/) (2024)
- [Counterfactual contrastive learning: robust representations via causal image synthesis](https://mlsanity.com/paper/2403.09605/) (2024)
- [Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2409.18872/) (2024)
- [Understanding differences in applying DETR to natural and medical images](https://mlsanity.com/paper/2405.17677/) (2024)
- [The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction](https://mlsanity.com/paper/2603.01250/) (2026)
- [Mitigating annotation shift in cancer classification using single image generative models](https://mlsanity.com/paper/2405.19754/) (2024)
- [MammoFlow: Multiview Mammogram Synthesis with Anatomically Consistent Flow Matching](https://mlsanity.com/paper/2606.28537/) (2026)

## Abstract (arXiv)

Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensitivity of mammography for breast cancer detection can be reduced by more than 20% in dense breasts. Additionally, extremely dense cases reported an increased risk of cancer compared to low-density breasts. This study aims to improve the mass detection performance in highdensity breasts using synthetic high-density full-field digital mammograms (FFDM) as data augmentation during breast mass detection model training. To this end, a total of five cycle-consistent GAN (CycleGAN) models using three FFDM datasets were trained for low-to-high-density image translation in highresolution mammograms. The training images were split by breast density BIRADS categories, being BI-RADS A almost entirely fatty and BI-RADS D extremely dense breasts. Our results showed that the proposed data augmentation technique improved the sensitivity and precision of mass detection in models trained with small datasets and improved the domain generalization of the models trained with large databases. In addition, the clinical realism of the synthetic images was evaluated in a reader study involving two expert radiologists and one surgical oncologist.