# Two-View Mammogram Synthesis from Single-View Data Using Generative Adversarial Networks
###### Abstract  
While two-view mammography taking both mediolateral-oblique (MLO) and cranio-caudual (CC) views is the current standard method of examination in breast cancer screening, single-view mammography is still being performed in some countries on women of specific ages. The rate of cancer detection is lower with single-view mammography than for two-view mammography, due to the lack of available image information. The goal of this work is to improve single-view mammography's ability to detect breast cancer by providing two-view mammograms from single projections. The synthesis of novel-view images from single-view data has recently been achieved using generative adversarial networks (GANs). Here, we apply complete representation GAN (CR-GAN), a novel-view image synthesis model, aiming to produce CC-view mammograms from MLO views. Additionally, we incorporate two adaptations--the progressive growing (PG) technique and feature matching loss--into CR-GAN. Our results show that use of the PG technique reduces the training time, while the synthesized image quality is improved when using feature matching loss, compared with the method using only CR-GAN. Using the proposed method with the two adaptations, CC views similar to real views are successfully synthesized for some cases, but not all cases; in particular, image synthesis is rarely successful when calcifications are present. Even though the image resolution and quality are still far from clinically acceptable levels, our findings establish a foundation for further improvements in clinical applications. As the first report applying novel-view synthesis in medical imaging, this work contributes by offering a methodology for two-view mammogram synthesis. murmogram; breast cancer; deep learning; generative adversarial network; multi-view image synthesis; novel-view image synthesis +
Footnote †: journal: Medical Image Analysis  
single-view mammography. This is opposite to the situations in Europe and the U.S., where some women younger than 50 undergo single-view mammography. While breast cancer incidence is higher among older women in US, the incidence peaks in the 45-49 age group in Japan [14]. The differences in the age-specific incidences of this cancer affect the number of views required for screening mammography. The important point is that CC-view mammograms are not necessarily obtained in breast cancer screening, whereas MLO-view mammograms must be taken. Unsurprisingly, the cancer detection rate with single-view mammography is inferior to those with two-view mammography [10; 11; 12; 15], as the breast cancer may be visually obscured by overlapping mammary glands in two-dimensional mammograms [16]. Therefore, it is sometimes hard to distinguish the overlapped cancer from surrounding tissues, particularly in single-view mammograms. In contrast, two-view mammograms can exhibit multiple appearances of overlapped shadows and guide identification of whether suspicious lesions are false-positive or true-positive cancer. We have no doubts that the diagnostic accuracy of single-view mammography would be improved if information from another view was available. Over the past decade, various image processing and image generation techniques using deep learning have been applied in medical imaging fields [17; 18; 19]. Most image generation models are based on generative adversarial networks (GANs) [20] and variational autoencoders (VAEs) [21]. Newer models, such as Wasserstein GAN (WGAN) [22], progressive growing GAN (PG-GAN) [23], and StyleGAN2 [24], have successfully been used to provide superior image quality and fidelity through more stable training. Some of the models have also been use to realistically simulate mammograms [25; 26; 27]. Recently, multi-view or novel-view image syntheses from a single image or limited-view images using GAN-based models have been exploited, particularly for human face and fashion images [28; 29; 30; 31; 32; 33]. [PERSON] et al.