# Adversarial Data Augmentation on Breast MRI Segmentation
## 1 Introduction  
Coping with small and poorly annotated datasets has been a recurring problem for medical image analysis researchers, limiting their success in validating supervised learning algorithms for real-life use. The paucity of comprehensive and annotated medical data is due to several factors: acquiring medical images often involves expensive and invasive procedures, and annotating them is a time-consuming task that requires the labor of experienced specialists. Additionally, these datasets are often unbalanced and lack variability since abnormal exams are captured less frequently than normal ones, contributing to unsatisfying performances in classification tasks. Images produced by traditional data augmentation techniques (e.g., rotation, translation, crop, shear) are often highly correlated with the already available ones, which makes this strategy insufficient to counteract the consequences of data scarcity. Furthermore, most of these alterations compromise the expected anatomy structure and positioning, producing unexpected or even distorted outcomes, which will be inadvertently and erroneously learnt. This, together with the data needs that accompanied the expansion of Deep Learning, served as motivations for the development of techniques to generate meaningful synthetic data and fast-forwarded research on this topic in recent years. One of the most promising approaches that emerged from that research is Generative Adversarial Networks (GANs) [1], which was already shown to be successful for the synthesis of natural images [2] and for super-resolution tasks [3]. The use of GANs to generate medical images is recent and, as such, to this date, only a limited set of imaging modalities and applications was explored. Still, important studies emerged over the past few years. Several works concerning the conversion across different parameter series or even whole imaging modalities have been proposed, such as from MRI to Computed Tomography (CT). Examples of MRI to CT conversion are classical GANs, trained with patches of images and applied in Auto-Context models [4; 5], and the usage of the CycleGAN to deal with unregistered datasets [6]. The CycleGAN is also used for multi-contrast MRI synthesis (T1 to T2 weighted conversion and vice versa) [7]. Lastly, the conversion of T1 and T2 weighted MRI images into corresponding magnetic resonance angiography representations was explored through the application of the steerable GAN, a variant of the PatchGAN which applies a steerable filter loss [8]. Other relevant applications of GANs to medical images include the usage of deep convolutional GANs for the generation of fake tumors in liver CT [9] and in multi-sequence brain MRI [10]. A super resolution GAN variation was also proposed to increase the resolution of brain MRI images [11]. Finally, MEDGAN [12], a cascade of U-Net blocks poses further material to be inspired from. Furthermore, of note is the image-to-image translation conditional GAN [13], known as Pix2 Pix, which has been applied for conversion between annotations and medical images. This framework was used for the synthesis of abnormal brain MRI images from annotations with a tumor label [14], and for the generation of retinal images from vessel masks [15]. A more recent work based on Contrastive Learning [16] has tried to both improve and generalize the image synthesis approach by disregarding the bijective assumption implicit in works using cycle-consistency and still avoid tailoring a loss function to the setup. In this work, we explore how some generative adversarial networks can be used for the synthesis of breast magnetic resonance images from corresponding annotations, for data augmentation purposes. Additionally, we contribute to the answering of the following questions:  
* **Generator architecture:** what generator architecture can enable the synthesis of more realistic images? * **Synthetic data effect:** what amount and level of generative quality is enough for an effective learning by a segmentation network?