Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms

• URL: http://arxiv.org/abs/2006.00086v1
• Date: Fri, 29 May 2020 21:23:00 GMT
• Title: Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms
• Authors: Eric Wu, Kevin Wu, William Lotter
• Abstract summary: 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.
• Score: 0.4297070083645048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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