# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 4 Discussion  
Breast tissue density is a known risk factor for cancer development, as women with denser breasts have a higher likelihood of developing cancerous regions compared to women with less dense tissue. However, abnormalities in the breast, whether malignant or benign, can often be concealed by the glandular and connective tissue, making it difficult for both radiologists and computer-assisted diagnosis systems to identify them early or during follow-up screening. Because connective tissue, glandular tissue, and malignancies all appear as white regions on a mammogram, cancerous regions may be hidden by healthy tissue. Our approach considers the density of the examined region of interest (ROI) by attempting to \"uncover\" and reveal what is masked by the tissue effect.  
In this study, we used two types of public screening mammography datasets, film and digital, to demonstrate the effectiveness of our proposed method. It is important to note that our proposed reverse transformation process based on CycleGANs only operates within the breast density mask and applies solely to those ROI patches that are incorrectly classified by classical CNN-based CAD systems as normal, benign, or malignant. Consistent with our experimental findings, the CycleGAN model successfully learned to translate ROI breast density from low to high (and vice versa) while preserving all domain features necessary for accurate type classification.  
In the film mammography datasets (MIAS, DDSM), the accuracy improvement is up to 12%, which can be attributed to the CycleGAN model's ACR reverse transformation process acting as an image enhancement step. However, the image quality of these datasets is inferior to that of fully digital ones (VinDR, SuReMaPP, INbreast), where the accuracy improvement is up to 6% at most, as these mammograms have better quality with no extreme intensity variations. When dealing with datasets acquired with equipment variations in hardware and time, the histogram transfer technique produces a good common reference and makes film mammography usable in mixed CNN-based solutions.  
Although CycleGANs often introduce artifacts in the output images (as seen in the lower right corner of patches in Figures 7 and 10), this behavior is expected and does not affect the validity of the artificial patches. These artifacts can be resolved with longer training epochs and more unpaired patch samples. In this work, we used 18K total unpaired ACR class patches (9K from class ACR-A, and 9K from classes ACR-B, -C, and -D, both real and artificial ones produced by the acGAN model), which took approximately six months on an RTX 3090/24 GB-based system for producing valid ACR class patches. The ACR heatmaps demonstrate that the transformed patches' density changes do not affect the patch's classification into the appropriate ACR class, providing macroscopic evidence of the process's ground truth.  
We did not optimize the CNNs/GAN topologies in terms of hyperparameters, but we attempted to keep our system's design relatively simple while enhancing its accuracy performance. More sophisticated and precise models can be deployed. Furthermore, our task, which involves transforming the ACR density of the examined ROI to reveal underlying findings, works particularly well for ACR density classes B and C, which account for 80% of all female breast cancer cases. For ACR-A, the density transformation back to the same class A contributes very little, as expected. Similarly, findings in class D density ROIs are much harder to identify, and the density transformation process should be augmented by extra information (such as BI-RAD characterization) for better diagnostic outcomes.