# Adversarial Data Augmentation on Breast MRI Segmentation
## 3 Experiments and Results
### Experiments  
#### 3.3.1 MRI Generation  
The main experiments focused on analyzing the generative capabilities of ResGAN, CycleGAN and Pix2 PixHD for our dataset. Apart from the initial hyper-parameter empirical setting, we experiment with the variation of batch sizes (BS) and the inclusion of Experience Replay [22]. Experience Replay consists of keeping an image buffer that desynchronizes the images that the Discriminator (\(D\)) and the Generator (\(G\)) receive on a given iteration. Most importantly, this enables \(D\) to occasionally review images of older iterations in an effort to keep it from overfitting to the current state of \(G\). Batch size tests were done for \(G\) with values 4, 8, 16, except for the CycleGAN architecture that had to be limited to one image per batch. Experience replay tests were done with a pool of 32 images, however, for similar hardware reasons, this test could not be performed coupled to Pix2 PixHD. All models were trained for 300 epochs, using a learning rate of 0.0002 for both the generator and the discriminator. The learning rate stayed constant for the first 150 epochs and was linearly decayed to 0 for the remainder epochs. Of the experiments run, batch size 8 presented the best, or similar results to those of BS 16. Table 2 presents the metrics for these best models, per architecture, while Figure 6 shows visual results. Figure 5: Examples from the Dataset, with background cropped for reader convenience. Although the objective of this work is to assess the effect of the synthetic images on the segmentation task, we tried to push forward those generative results that were visually closest to the original dataset. We largely assumed that a more faithful image set would induce a more reliable segmentation model. Among the first experiments, was the usage of the original Pix2 Pix architecture and its subsequent upgrading to a double GAN, cycle-consistent approach. The results for both versions were unimpressive at best. The generated images consisted of intensity blurred versions of the annotation-like input data, with no minimal texture similarity to that of the target MRI. The \(D\) losses always managed to quickly go to zero, which signaled its inability to properly enter an adversarial competition. We concluded that the U-Net skip connections did more harm than good and the common loss functions used were not enough to sway this architectural auto-correlatory bias. Here, the smaller scale nature of the residual blocks, present in other of the Pix2 Pix variants used, and the absence of those encoder-decoder arching skip connections managed to produce more visually appealing results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**SSIM\(\uparrow\)**} & \multicolumn{2}{c}{**FID\(\downarrow\)**} \\ \cline{2-6}  & \(\perp\) & \(\mu\) & \(\sigma\downarrow\) & \(\top\) & \\ \hline Pix2 PixHD BS8 & 0.778 & 0.849 & 0.041 & 0.951 & 167.0 \\ \hline ResGAN BS8 & **0.799** & **0.874** & **0.036** & **0.961** & 206.3 \\ \hline ResGAN BS8 \(+\) ExpReplay & 0.798 & 0.873 & **0.036** & 0.960 & 213.8 \\ \hline CycleGAN BS1 \(+\) ExpReplay & 0.764 & 0.844 & 0.041 & 0.953 & **157.4** \\ \hline \hline \end{tabular} SSIM—Structural Similarity Index, range \(-1\) to 1, best is max, negative is inverted, averaged sample metric; FID—Frechet Inception Distance, range 0 to Inf, best is 0, set metric.  
\end{table} 
Table 2: Generation test results (170 images). Presents the minimum (\(\perp\)), mean (\(\mu\)), standard deviation (\(\sigma\)) and maximum (\(\top\)) values of SSIM and the FID results for each network, with respective batch sizes (BS). Figure 6: Best visual test set generation results and respective SSIM. ResGAN and Pix2 PixHD with BS8. CycleGAN with BS1 and Experience Replay. More results on Appendix A Figures A1 and A2. We noticed that, for all the Pix2 Pix based models used, the channel concatenation of between the \(G\) input and the synthetic output was passed to the \(D\)'s input.