# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 1 Introduction
mammograms, which could be caused by mistakes or tired observers. Convolutional neural networks (CNNs) have gained popularity in recent years for a variety of image processing classification tasks. CNN-based CAD systems have proven to be quite effective, with high rates of breast cancer diagnosis. However, there are still some open issues in the automatic breast cancer detection problem, one of the most important being the breast density as already described earlier. In the literature, the use of generative adversarial networks (GANs) for numerous medical challenges, including data synthesis and augmentation, is constantly growing. However, these models may experience numerous artifacts (i.e., checkerboard artifacts), which may affect the quality of the final synthesized images, especially when working with full-size mammograms. GANs can help in the synthesis of a variety of plausible-looking mammography images either in full size [10, 11, 12, 13] or in ROI-based approaches [14, 15]. Due to its impact on the accurate detection of cancer in mammograms, the problem of automatic breast tissue recognition has been extensively studied over the last decade, with a large number of papers published in this area proposing systems that use either traditional machine learning techniques or, more recently, deep learning networks and architectures [4, 5, 6, 7, 13, 15, 16]. However, to our knowledge, none of them proposes a method to \"transform\" breast density to lower density levels and thus enhance the diagnostic accuracy of CAD systems. Motivated by the crucial role that breast tissue density plays in the detection of breast cancer, as it makes it more challenging for radiologists to accurately detect cancerous regions in mammograms, we sought to investigate to what extent computer-assisted diagnosis systems are affected by this challenge, using different types of mammograms ranging from scanned film to fully digital images in our experiments. To address this, we propose a novel breast tissue transformation using CycleGAN network topology that can be applied to any region of interest (ROI) to adjust its density to match the characteristics of an ACR-A class tissue, which is easier to diagnose successfully. CycleGAN was chosen due to its widespread applications and accomplishments in the field of cancer imaging [16]. A crucial methodological characteristic of CycleGAN is that it can train on unpaired data without the need for matching image pairings in the source and target domains. As our datasets lack image pairings and the same patient's breast cannot belong to both the high and low breast density domains, using unpaired training data confirmed that CycleGAN would work with our datasets. The main contribution of our system is taking breast density into account, and by using a CycleGAN model, transforming the density of the ROI's tissue to match the characteristics of an ACR-A class tissue. This process significantly improves recognition accuracy while reducing the number of undetected ROIs due to their dense breast tissue. The structure of the paper is as follows: In Section 2, we give a detailed description of the proposed CAD system. We present the different modules that it consists of and the different datasets that were used to test the efficacy of the system. In Section 3, we present the experimental setup and results, which are further discussed and commented in Section 4. Finally, in Section 5, some conclusions and remarks are given concerning the limitations and further research.