# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 5 Conclusions  
Possible color inconsistencies and their implications in histopathology images have become a critical issue. Various stain normalization techniques have been proposed to condense the tissue inconsistencies. These techniques are paramount to accurate histopathology image analysis systems and guide the pathologists in their visual diagnostics. Normalization techniques have been used under the assumption that training and test sets of a dataset are of the same style characteristics. However, appearance variations in stained histopathology images originating from different pathology centers violate the above as  
Figure 8: Distribution of uncertainty for the ICIAR 2018 breast histopathology images. (**a**) Aleatoric uncertainty for entropy measure. (**b**) Epistemic uncertainty for \(M1\) measure. The \(x\)-axis shows the total number of cancer classes of this dataset and \(y\)-axis shows the amount of uncertainty.  
sumption. The main objective of color normalization process is to match the color patterns of the source and target domains images for the cancer classification task.  
In this paper, we considered the color patterns of images as style patterns and carried out color transfer across the datasets. We designed a novel fully trainable framework named stain acclimation generative adversarial network (SA-GAN) that consists of one generator and two discriminators trained with two adversarial losses and one textural loss. The generator generates the images by obeying the distribution of the target domain. The discriminators enforce the generated images to modify color patterns and structure contents. In model training, color attribute metric helps the model to correctly learn the image-specific color patterns. The designed model performed an efficient mapping between the data domains by involving entire domain distribution and not altering the tissue structure of images. The empirical evaluations on four different histology datasets reveal the consistent normalization performance of proposed SA-GAN in comparison to state-of-the-art methods. The colors and contrast are exactly preserved in the processed images, which is most desirable for any color normalization method. The potential contribution of the proposed algorithm can be perceived collectively from efficient color normalization results, and improved classification performance for histopathology.  
Conceptualization, T.K., A.K. and M.S.; methodology T.K., A.K.; software, T.K.; validation, M.A.A., M.W., M.A.A.; formal analysis, M.Z.S., A.U.H.; investigation, M.F.S., T.K. and I.R.; resources, A.U.H.; data curation, M.A.A. and A.U.H.; writing--original draft preparation; writing--review and editing; visualization; supervision; project administration T.K., A.K., M.W. and M.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the Shenzhen Fundamental Research Project under Grant JCYJ20170412151226061, Grant JCYJ20170808110410773, and Grant JCYJ20180507182241622.  
Not applicable.  
We used MITOS-ATYPIA-14 [33], TUPAC 2016 [35], ICIAR 2018 [34], MICCAI'16 [5] datasets in this study.  
The authors declare no conflict of interest.