# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 4 Experimental Results
### Structure Analysis
From visual analysis, it is clear that our method overcomes all the limitations of prior proposed conventionalmethods. It is mainly due to the involvement of color distributions of entire image domain as well as the use of the color attribute metric. Figure 3: Visual results of color normalization algorithms SCD (2016), Macenko (2016), ACD (2016), [PERSON] (1971), StainGan (2018) and Proposed SA-GAN on MITOS-ATYPIA-14 ([PERSON], 2016), ICIAR 2018 ([PERSON], 2018), MICCAI’16 ([PERSON], 2018) datasets. “Source” corresponds randomly selected original training images; “Target” corresponds to original test image chosen from various datasets. Normalized images obtained with the proposed method are given in the last column. Proposed SA-GAN involves color distributions of entire image domain rather to rely on single image. The quantitative color normalization performance of tested methods on various datasets is given in Tables 1 and 2. In comparison to the previous methods, our method shows remarkable PSNR on all datasets. The evaluated parameters (PCC and SSIM) with prior proposed methods, widely deviate from one for all breast and colon datasets. Lower and positive values of PCC and SSIM are non-ideal. Small PCC values demonstrate that color inconsistencies are still present in processed images. Similarly, the low value of SSIM obtained with other methods indicates that structure details are not well preserved in stain normalized images. The large difference between values of SSIM and PCC represents that the normalization methods are inconsistent in producing images with accurate contrast. Some of the processed images have enhanced contrast while other images have low contrast value. Compared to state-of-the-art algorithms, our method not only obtained high PCC and PSNR values but also achieved a remarkable value of SSIM. The quantitative (Tables 1 and 2) results reveal that the designed algorithm outperforms all prior and recently proposed state-of-the-art color normalization algorithms [16; 17; 20; 23; 26]. For our method, the measured SSIM and PCC values (Table 2) are very close to one for all datasets. In visual analysis in Figure 3, images that show accurate stain representations are considered the best stain normalized results. The visual assessment shows that significant improvements in color consistency occur and no apparent artifacts are found in the results of the SA-GAN model. From qualitative results, it is also apparent that our designed stain transfer algorithm is robust and applicable to H&E-stained histology images. Sensitivity towards different target images: In this analysis, the sensitivity of color mapping methods to choose the target image is checked. We selected three different target images from ICIAR 2018 dataset [34]. Correspondingly, three different color attribute metrics are computed from selected images. The SA-GAN model is separately trained using computed color metrics. We did not observe any change in SSIM value by changing the color metrics. However, color attribute constraint information assists in adjusting the colors of generated images properly. It is noticed that previously proposed color mapping methods [16; 17; 20; 26] are sensitive towards the selection of target images. Results evaluated with other methods represent the change in SSIM regarding target images (Figure 4). This is because these methods use a single image in color normalization process, whereas our designed strategy takes advantage of whole dataset distributions as well as color statistics of a single target image instead of just relying on a single image.