# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 5 Conclusions  
Our study presents a new computer-aided diagnosis (CAD) system for breast cancer that can classify suspicious regions of mammograms into three categories: normal, benign, and malignant. Our aim is to improve the accuracy of this classification by taking into account the density of the patient's breast tissue. Dense breasts are more likely to have invasive ductal carcinoma due to the increased amount of glandular tissue, which can make it harder to detect abnormalities. Our proposed CAD system solves the problem of \"masking\" in mammograms, where dense breast tissue can hide abnormalities. We achieve this by using a process that reverses the effects of breast density on mammograms. As we hypothesized, the CycleGAN models not only learned how to translate from low-to-high breast density but also preserved the domain characteristics during translation. However, the present study is not without limitations. One limitation of our work involves the availability of healthy ACR-D mammograms to train the generative models. There are not many annotated datasets publicly available that are fully digital, so one must resort to a closed set of mammograms. Another limitation is the imbalanced nature of the problem; the ratio between the different ACR classes is by no means equally distributed. In our approach, this was partially resolved by using image manipulation methods (i.e., GANs) that produced artificially patched images via domain adaptation. However, it is known that this process does not extend the feature space of the problem but rather produces structurally similar images, which, in many cases, result in overfitting. Although the sophisticated mathematics underlying deep learning training algorithms is conceptually understandable, their architectures are more of a \"black box\" paradigm. In the case of the breast density CycleGAN, one must comprehend the learned mapping via post hoc explainability [42]. The post hoc explanation is a task that will be performed in future work. We also plan to conduct further testing of our proposed system using real clinical images and interpret the transformations performed by the CycleGAN with the assistance of a team of radiologists. Although more data are needed to fully examine the extent of this reverse process, in all datasets that we tested, the overall percentage of successful recognition for normal/benign/malignant ROIs was improved significantly. In future work, we plan to expand this method to the whole breast region, by using more advanced GANs and CNNs that can analyze the relationships between neighboring areas. Our approach could help doctors and radiologists identify suspicious regions and plan treatments at an early stage, potentially avoiding consequences and treatment difficulties. Conceptualization, D.A., G.A. and A.K. ; methodology, A.K. ; software, D.A., G.A. and I.C. ; validation, G.A. and I.C. ; formal analysis, D.A. and A.K. ; investigation, D.A., G.A. and I.C. ; resources, G.A. and I.C. ; data curation, D.A. and G.A. ; writing--original draft preparation, D.A. and A.K. ; writing--review and editing, D.A. and A.K. ; visualization, G.A. and I.C. ; supervision, A.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Digital mammography screening: Sensitivity of the programme dependent on breast density. _Eur. Radiol._**2016**, _27_, 2744-2751. [CrossRef] [PubMed]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Volumetric breast density affects performance of digital screening mammography. _Breast Cancer Res. Treat._**2016**, _162_, 95-103. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks. _Eur. Radiol._**2023**, 1-8. [CrossRef]
* [PERSON] et al.