# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 4 Experimental Results
### Ablation Experiments  
The normalization performance is influenced by the architecture of the generative adversarial network model [27]. Recall that the architecture of SA-GAN consists of one generator and two discriminators which are trained by three different losses \(\mathcal{L}_{\text{D1}}\), \(\mathcal{L}_{\text{D2}}\), and \(\mathcal{L}_{\text{T}}\). However, the question arises, as to why we need two discriminators instead of one. Is the structural loss \(\mathcal{L}_{T}\) important? Is the color attribute metric necessary to maintain color contents in generated images? What will be the possible results if we combine the structural loss \(\mathcal{L}_{\text{T}}\) and color attribute metric with other GAN structures such as StainGAN? To address these queries, ablation experiments are conducted. The CV-NMI and CV-NMH for different configurations of the proposed SA-GAN are shown in Table 4. The following four configurations are designed for comparison. (i) \((SA-GAN)-D_{2}\) represents the configuration without second discriminator \(D_{2}\), (ii) \((SA-GAN)-\mathcal{L}_{T}\) is configuration when SA-GAN is trained without involving structural loss \(\mathcal{L}_{T}\), (iii) \((SA-GAN)-C\) is configuration when SA-GAN is trained without color attribute metric constraint, and (iv) \(StainGAN+\mathcal{L}_{T}+C\) is configuration when StainGAN [28] is trained with structural loss and color attribute metric. Figure 5: Polar plots of the CV-NMI (**a**) and CV-NMH (**b**) for proposed SA-GAN amongst the normalized training datasets and un normalized training datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Normalization** \\ **Method** \\ \end{tabular} } & \multicolumn{2}{c}{**MITOS-ATYPIA-14**} & \multicolumn{2}{c}{**TUPAC 2016**} & \multicolumn{2}{c}{**ICIAR 2018**} & \multicolumn{2}{c}{**MICCAI716**} \\ \cline{2-9}  & **CV-NMI** & **CV-NMI** & **CV-NMI** & **CV-NMI** & **CV-NMI** & **CV-NMI** & **CV-NMI** \\ \hline No normalization & 0.311 & 0.214 & 0.347 & 0.312 & 0.332 & 0.314 & 0.213 & 0.233 \\ SCD [16] & 0.142 & 0.132 & 0.123 & 0.102 & 0.131 & 0.120 & 0.103 & 0.124 \\ Macenko [20] & 0.089 & 0.092 & 0.0901 & 0.083 & 0.082 & 0.078 & 0.087 & 0.082 \\ ACD [26] & 0.071 & 0.089 & 0.073 & 0.871 & 0.078 & 0.0871 & 0.076 & 0.076 \\ Reinhard [17] & 0.042 & 0.030 & 0.042 & 0.021 & 0.043 & 0.034 & 0.033 & 0.032 \\ StainGAN [28] & 0.031 & 0.029 & 0.035 & 0.032 & 0.298 & 0.032 & 0.027 & 0.028 \\ SCAN [23] & 0.041 & 0.032 & 0.039 & 0.047 & 0.040 & 0.035 & 0.039 & 0.038 \\ Proposed SA-GAN & 0.012 & 0.017 & 0.021 & 0.018 & 0.011 & 0.010 & 0.014 & 0.021 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation of stain accumulation in terms of CV-NMI and CV-NMH. As reflected from quantitative results (Table 4), the first four configurations poorly performed the color normalization. To further investigate, the effect of network architecture sample results for different settings of the design are given in Figure 6. As per results, the use of dual discriminators with adversarial losses is important and can achieve a desirable normalization consistency. Two discriminators deal with structure preserving and color transferring properties. It is important to note that color attribute constraint information assists the model in properly adjusting the colors from source domain to target domain. However, there is no substantial difference in metrics values, even when we exclude the color attribute information (Table 4 and Figure 6). This analysis proves that the proposed SA-GAN shows small sensitivity towards single target image in contrast to other color mapping methods [16; 17; 20; 26]. It does not just rely on a single target image, rather involves the color distribution of whole dataset. Meanwhile, structure loss is also necessary to train the generator that is consistent with the results. Color distributions of test images obtained with SA-GAN are better matched with training images.