# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Deep Learning Methods for BC Detection and Classification
#### 2.2.3 Generative Adversarial Networks  
In ML, generative modeling refers to the task of discovering and understanding the patterns or regularities in input data without any supervision. The objective is to develop a model that can create or produce new instances that are similar to the original dataset. One way to accomplish this is through the use of generative adversarial networks (GANs). One of the challenges in BC detection is the limited availability of large-scale datasets, which can make it difficult to train accurate ML models. GANs can overcome this challenge by generating synthetic images that closely resemble real medical images. The synthetic images can be used to augment the dataset, increasing the sample size and improving the accuracy of the models trained on the augmented dataset. Additionally, GANs can be used to generate different views or angles of a breast tumor, which can help in detecting cancerous regions that might be difficult to see in traditional 2D medical images. GANs can also be used to simulate different imaging modalities, such as ultrasound [26], and MRI [27], which can provide additional information and improve the accuracy of breast cancer diagnosis. Simulated mammograms can be created with GANs [28]. The study aimed to detect mammographically-occult (MO) cancer in women with dense breasts and a conditional generative adversarial network (CGAN) was developed to simulate a mammogram with normal appearance; a CNN was trained to detect MO cancer. The study found that using CGAN-simulated mammograms improved the MO cancer detection when compared to using only real or simulated mammograms. Another study that emphasized the use of GANs for mammographic images augmentation is described in [29]. The GAN-generated images were compared with affine transformation augmentation methods, and the results showed that the GAN approach performed better in preventing overfitting and improving classification accuracy. However, the study also highlighted that GAN-generated or augmented images should not substitute real images in training CNN classifiers due to the risk of overfitting. The gap in the availability of training datasets is overcome by using GAN to synthesize data similar to real sample images in [30]. However, current publicly available datasets for characterizing abnormalities in digital mammograms do not contain sufficient data for some abnormalities. This paper proposes a GAN model, named ROImammoGAN, which synthesizes ROI-based digital mammograms to learn a hierarchy of representations for abnormalities in digital mammograms. The proposed GAN model was applied to MIAS datasets, and the performance evaluation yielded a competitive accuracy for the synthesized samples. Another study that synthetized high-resolution mammograms, enabling user-controlled global and local attribute-editing of the generated images is presented in [31]. The model was evaluated in a double-blind study by expert mammography radiologists, achieving an average AUC of 0.54 and indicating the potential use of the synthesized images in advancing and facilitating medical education. To overcome the challenges of limited size and class imbalance of publicly available mammography datasets, the study in [32] proposed a synthetically augmented the dataset by adding lesions onto healthy screening mammograms. The authors trained a class-conditional GAN to perform contextual in-filling and experimentally evaluated its effectiveness in improving BC classification performance using a ResNet-50 classifier. The results show that GAN-augmented training data improves the AUC compared to traditionally augmented data, demonstrating the potential of this approach. However, there are some limitations in using GAN towards BC applications, such as data availability, as GANs require large amounts of high-quality data to train effectively. GANs can also be notoriously difficult to interpret, and it can be challenging to understand how the network arrived at its decision; this can be difficult for a medical-related application.