# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 1 Introduction  
Breast density is considered to be a measure of fibrous and glandular tissue existence (also known as fibroglandular tissue) in the whole breast when compared to the fat tissue. It has no direct relation to the breast size or its firmness. There are three basic components of a breast: connective tissue, ducts, and lobules. The connective tissue, which is formed of fatty and fibrous tissue, envelops and holds everything in place. Lobules are the small glands that produce milk, while ducts are the tiny transport tubes that carry milk from the lobules to the nipple. Together, lobules and ducts are known as glandular tissue (Figure 1). Fibrous tissue and fat give breasts their size and shape while they also hold the rest of the breast components in place. Most breast cancers begin in the ducts or lobules. Breast density as a measurement is important mainly for two reasons: Women who have dense breast tissue, present a higher risk of developing breast cancer compared to women with less dense tissue. It is unclear until now why high density is associated with higher breast cancer risk. This may be attributed to the fact that dense breast tissue is formed by a bigger proportion of cells that could be evolved into abnormal under some conditions. The absolute relationship between the risk factor of breast cancer development in women with increasing breast mammographic density has already been reported in many relevant studies [1]. The second reason is that dense breast tissue (fibrous and glandular) makes it harder for radiologists to detect cancerous regions in mammograms because it looks white or opaque on an MRI. Breast masses and cancerous regions also share the same dominant white characteristics as the rest of the healthy tissue, so density makes it harder for the abnormalities to be traced by radiologists or computer assisted diagnosis (CAD) systems. In contrast, a fatty tissue looks almost black, so it is easier to see abnormalities that have large intensity values in a low intensity background. In screening mammography, according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS), there exist four different levels of density [2]. Almost entirely fatty indicates that breasts are almost entirely composed of fat (ACR-A). Scattered areas of fibroglandular density indicate there are some scattered areas of density, but most of the breast tissue is non-dense (ACR-B). Heterogeneously dense indicates that there are some areas of non-dense tissue, but most of the tissue is dense (ACR-C). Finally, extremely dense indicates that nearly all breast tissue is dense (ACR-D). Breast density cannot be detected through physical examination but only through mammography, and it is an important variable that affects the sensitivity of mammography [3; 4; 5; 6]. Over 40% of women with dense breast tissue are characterized as heterogeneously dense (ACR-C) or extremely dense (ACR-D). Dense breast tissue is an independent risk factor for the development of abnormalities and decreases the likelihood of breast cancer being detected successfully on screening mammography, leading potentially to delayed diagnosis, which can have detrimental results. In order to automatically identify and categorize breast lesions in mammograms using traditional machine learning models and to bring these results to doctors' attention, computer-aided detection systems (CAD) were developed in the 1990s [7; 8; 9]. Yet, due to their low specificity, current conventional CAD systems are unable to considerably increase screening performance. The success of these algorithms to identify and categorize abnormalities in mammograms is related to specificity. This differs from diagnosis, which draws conclusions about the cause of an aberration. It is crucial to find irregularities in  
Figure 1: Overview of a typical breast. (Adapted from here. Original image by [PERSON], medical illustrator; [PERSON], MD, cardiologist, is licensed under the Creative Commons Attribution 2.5 Generic license).