# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 4 Experimental Results
### Evaluation by Classification  
Ultimately the effect of stain color normalization of histopathology images on the CAD system has been checked. Recently, several studies have utilized convolutional neural networks (CNNs) for histological image analysis [11; 43; 44]. Literature also shows that stain color normalization of histological images can increase the performance of deep CNN models [44; 45; 46]. We conducted this experiment to assess the significance of stain normalization methods for CNN based classification model. In this analysis, multiclass breast histology image classification were performed. We adopted the deep Resnet50 [30] image classification model and applied variational dropout [47]. At inference time, for each tested image, the model predicts the class \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MITOS-ATYPIA-14**} & \multicolumn{2}{c}{**TUPAC 2016**} & \multicolumn{2}{c}{**ICIAR 2018**} & \multicolumn{2}{c}{**MICCAT16**} \\ \cline{2-9}  & _Corr_\(\uparrow\) & _Dist\_Bhat_\(\downarrow\) & _Corr_\(\uparrow\) & _Dist\_Bhat_\(\downarrow\) & _Corr_\(\uparrow\) & _Dist\_Bhat_\(\downarrow\) & _Corr_\(\uparrow\) & _Dist\_Bhat_\(\downarrow\) \\ \hline Train-Test & 0.101 & 0.813 & 0.098 & 0.830 & 0.121 & 0.867 & 0.110 & 0.820 \\ \hline Train-NormTest-SCD & 0.231 & 0.615 & 0.211 & 0.601 & 0.122 & 0.667 & 0.202 & 0.570 \\ \hline Train-NormTest-Macenko & 0.321 & 0.598 & 0.310 & 0.521 & 0.223 & 0.571 & 0.220 & 0.541 \\ \hline Train-NormTest-ACD & 0.329 & 0.498 & 0.311 & 0.587 & 0.310 & 0.581 & 0.310 & 0.613 \\ \hline Train-NormTest-Reinhard & 0.412 & 0.408 & 0.347 & 0.488 & 0.300 & 0.511 & 0.247 & 0.629 \\ \hline Train-NormTest-StaitCan & 0.428 & 0.380 & 0.337 & 0.476 & 0.330 & 0.455 & 0.341 & 0.521 \\ \hline Train-NormTest-SCAN & 0.410 & 0.420 & 0.339 & 0.398 & 0.371 & 0.361 & 0.332 & 0.421 \\ \hline
**Train-** & & & & & & & & \\
**NormTest-Proposed model** & **0.612** & **0.208** & **0.597** & **0.236** & **0.520** & **0.211** & **0.586** & **0.319** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Train-Test datasets color consistency analysis in terms of histogram correlation and Bhattacharyya distance using the proposed technique with other state-of-the-art methods. probabilities and also provides a measure of uncertainty. The classification model differentiates breast histology images into four classes: Normal, Benign, In situ, and Invasive carcinoma. For this experiment, we divided the 400 training images into validation (100 images) and training (300 images) sets. For this experiment, we treated the validation data as an unseen test set. The small patches of 224 \(\times\) 224 pixels were extracted from 2048 \(\times\) 1536 pixels HPF images using a step size of 30 pixels. In the patch extraction process, about 24,000 patches were generated from 300 training images. The ResNet model [30] was separately trained on patches generated from original set of images and images processed by stain normalization methods [16; 17; 20; 23; 26; 28]. The precision-recall curve of the ResNet classification model to compare color normalization methods [16; 17; 20; 23; 26; 28] on the validation dataset is shown in Figure 7. The classification model achieves the best detection results on stain normalized images obtained with the SA-GAN model compared to the normalized images by other methods, showing a 5.5% improvement on this part of data. In comparison to state-of-the-art normalization methods [16; 17; 20; 23; 26; 28], our method effectively maintains the structural and color details of normalized images and provides benefits to achieve robust performance in the classification of histological images. Classification performance on hidden test data: The class labels of the ICIAR test dataset are not publicly available (hidden by the challenge organizers).