# Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms

arXiv: https://arxiv.org/abs/2006.00086 · HTML: https://mlsanity.com/paper/2006.00086/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2020.

## Built on by (most cited first, 5 of 5)

- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica](https://mlsanity.com/paper/2107.11696/) (2021)
- [Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease](https://mlsanity.com/paper/2106.01416/) (2021)
- [An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis](https://mlsanity.com/paper/2406.14735/) (2024)
- [Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets](https://mlsanity.com/paper/2607.06549/) (2026)

## Abstract (arXiv)

Data scarcity and class imbalance are two fundamental challenges in many machine learning applications to healthcare. Breast cancer classification in mammography exemplifies these challenges, with a malignancy rate of around 0.5% in a screening population, which is compounded by the relatively small size of lesions (~1% of the image) in malignant cases. Simultaneously, the prevalence of screening mammography creates a potential abundance of non-cancer exams to use for training. Altogether, these characteristics lead to overfitting on cancer cases, while under-utilizing non-cancer data. Here, we present a novel generative adversarial network (GAN) model for data augmentation that can realistically synthesize and remove lesions on mammograms. With self-attention and semi-supervised learning components, the U-net-based architecture can generate high resolution (256x256px) outputs, as necessary for mammography. When augmenting the original training set with the GAN-generated samples, we find a significant improvement in malignancy classification performance on a test set of real mammogram patches. Overall, the empirical results of our algorithm and the relevance to other medical imaging paradigms point to potentially fruitful further applications.