# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 2 Literature Review  
In literature, a large number of state-of-the-art algorithms exist for color standardizing of histopathology images. The color deconvolution methods estimate the staining matrix to decompose RGB images into staining components [15]. The staining matrix represents the concentration of each color stain and can be computed based on image statistics. One of the first non-adaptive color deconvolution algorithm, [15] was proposed to empirically estimate the stain color matrix for hematoxylin and eosin stains. However, this algorithm worsens the normalization performance due to the approximate estimation of stain vectors. [PERSON] et al. [16] introduced a supervised approach to quantify the stain concentration matrix using pixel-level statistical color descriptors (SCD). They used a nonlinear color mapping phenomenon to perform normalization of source image to target image color space. This method involves high computational complexity compared to other state-of-art techniques. [PERSON] et al. [17] proposed a color mapping method in LAB color space. In his method, each color channel of source image is aligned to the color channel of user selected template image. After performing the color transformation, the standardized images are converted back to RGB color space. This color matching technique assumes that the proportion of tissue components for each dying agent is similar across the dataset. However, generally, dyes have independent contributions to various tissue images. Consequently, the method by [PERSON] et al. [17] leads to improper color matching where the white background is mapped as colored region. [PERSON] et al. [18] designed a fuzzy based modified Reinhard (FMR) color normalization method to control color coefficients and enhance the contrast of histopathology images. They employed fuzzy logic to overcome the limitation of the conventional [PERSON] et al. [17] method. Recently, [PERSON] et al. [19] proposed a normalization method to reduce the color and stain variability in H&E-stained histopathological images. This method also involves fuzzy logic for illumination, stain, and spectral normalization. Another algorithm by [PERSON] et al. [20] finds the singular value decomposition (SVD) values in the optical density space and projects the data onto the plane that corresponds to the two largest singular values. This technique can be applied to other histological stains and implicates low computational complexity. However, it becomes possible to wrongly estimate the stain vectors when the intensity of the stained region becomes higher than the imposed threshold (\(\beta=0.15\)). [PERSON] et al. [21] proposed a normalization approach based on the previously introduced spatially constrained mixture model [22]. In [22], a multivariate skew-normal distribution was used to quantify symmetric and nonsymmetric distributions of the stain components and to estimate the parameters. Recently, [PERSON] et al. [23] proposed an unsupervised normalization technique named the stain color adaptive normalization (SCAN) algorithm. The SCAN algorithm is based on segmentation and clustering strategies. In addition, [PERSON] et al. [24] proposed a method for blind color deconvolution of histology images based on the total variation (TV) technique. They used variational Bayesian algorithm to compute stain concentration and color matrix. The presence of strong color variations in H&E-stained histology images cause the failure of this algorithm. Recently,Hoque et al. [25] designed a color deconvolution technique to quantify the stain components from H&E-stained images. The Retinex model [25] determines an illumination map that is constructed using the maximum intensity of RGB color channels. [PERSON] et al. [26] proposed an algorithm named adaptive color deconvolution (ACD) for stain separation and color normalization of whole slide images (WSIs).