# Adversarial Data Augmentation on Breast MRI Segmentation
## 3 Experiments and Results
### Experiments
#### 3.3.2 Semantic Segmentation  
The segmentation part of the pipeline was performed over the original dataset, augmented with the synthetic images alternatively coming from each of the generative models. A factor of augmentation (\(p\)) was varied, ranging from just the original set (\(p=0\)), to a set with as much real as synthetic data (\(p=0.5\)). A more in-depth study regarding the effects of \(p\) is presented in Section 4.3.  
Every model was trained for 75 epochs, using a learning rate of 0.0001. Like with the generative models, the learning rate stayed constant for the first half of the epochs and was linearly decayed to 0 for the remainder epochs.  
The U-Net segmentation model resulted in the outcomes shown in Tables 3 and 4 and Figure 7.  
The original code from the U-Net used [33] employed a cross-entropy loss function. We found that, due to the natural label imbalance of MRI volumes, cross-entropy loss, along with the single channel multi-label approach was not reaching sufficient results. We experimented briefly with cross-entropy loss coupled to a multi-channel output (a channel per label) setup, but results were also lackluster. After that, we promptly changed to DICE loss that yielded better results, and later to softDICE with the logit normalization trick (Section 2.3), to enhance training nuances and enable quicker learning and more accurate outcomes. Following this trajectory, and still somewhat concerned with balancing issues during training, we looked into replacing the DICE with, the related, Tversky Loss [29] (Section 2.3), tilting the overall metric towards False Negative reduction. With this we aimed to compensate the less frequently appearing labels (\(\approx\)10%). We found that, in our setup, the Tversky loss did not provide significant change to results, and thus, we opted to continue with softDICE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Synthesis Generator**}} & \multirow{2}{*}{**Best \({}^{*}\)\(p\)**} & \multicolumn{4}{c}{**DICE \(\uparrow\)**} \\ \cline{3-6}  & & \(\perp\) & \(\mu\) & \(\sigma\) & \(\top\) \\ \hline No Aug & 0 & 0.495 & 0.806 & 0.079 & 0.949 \\ \hline Pix2 PixHD & 0.333 & 0.459 & 0.803 & 0.082 & 0.954 \\ \hline ResGAN & 0.333 & **0.504** & **0.813** & **0.077** & 0.956 \\ \hline CycleGAN & 0.15 & 0.483 & **0.813** & 0.081 & **0.957** \\ \hline \hline \end{tabular} \(p\)–augmentation factor: 0–only original set, 0.5–original and synth sets in \(50/50\); \({}^{*}\) Best during validation phase.  
\end{table}
Table 3: Global segmentation test scores. Presents the minimum (\(\perp\)), mean (\(\mu\)), standard deviation (\(\sigma\)) and maximum (\(\top\)) values of SSIM for the U-Net trained with the best synthset of each respective generator. The best no augmentation baseline (_No Aug_) is also shown.  
An additional experiment coupled the DICE loss with a relevance map, in which we weight differently regions of the label masks. We noticed that after the inclusion of the DICE loss some, fairly obvious, breast label portions started to be classified as background. Some holes started to appear in the middle of that label. On the other hand, the posterior regions that also included breast and pleura labels were correctly being predicted, even though the available labels did not include those portions. The model was correctly propagating the concept. We wanted to reward this behaviour and punish the middle of breast hole misclassification. To do so, we developed a relevance map, reducing the relative weight of the posterior portions of background label, starting from the most posterior pleura pixels. However, the visual outcomes did not provide obvious enhancements as this tradeoff improved some cases but worsened others. We opted to discard this concept on other experiments.