# SA-GAN: Stain Aclimation Generative Adversarial Network for Histopathology Image Analysis
## 2 Literature Review
The ACD model [26] is an optimization strategy to estimate the stain separation parameters by considering different prior knowledge of staining (i.e., proportion of stains and intensity). The advantage of this approach is to reduce the failure rate of normalization, but it involves high optimization costs. As per the literature, two main types of color normalization methods exist: standard color deconvolution methods and generative adversarial network (GAN) based color transfer methods. In standard color deconvolution methods, a single image is selected as a reference image and color distributions of all source images are mapped to that single image. It is accepted that if someone selects a single image the color characteristics of that image are copied to all source images. This type of color transformation creates color artifacts in the processed images. Recently, GAN-based methods effectively solve the problems of image super-resolution, reconstruction, and segmentation, and are widely used for many medical image analysis tasks. Additionally, different generative adversarial networks (GANs)- [27] based stain transfer techniques [28; 29; 30; 31] have been proposed for color normalization of histopathology images. Although the simple GAN network shows significant performance on natural images; it is however, not proficient to maintain the structural contents in histopathology images. The aforementioned GAN-based methods involve a group of images in color transfer process. So, they efficiently learn dataset-specific properties but ignore image-specific color patterns in the histopathology images. In this paper, we propose a novel design that modeled the stain normalization as an adversarial game and transfer stains across datasets, originating from different pathology laboratories with different staining appearances. Specifically, we aim to design a fully trainable framework that not only transfers stains across the datasets but also learns to easily adjust color attributes of processed images. The trained model would ultimately condense the stain variations in image datasets by leveraging dataset distributions as well as color attributes details extracted from the reference image. It is important to clarify that color attribute metrics are employed to control the color and contrast characteristics of the generated images. Using the color attribute details, our proposed method easily adjusts color contents and preserves structural details in the processed images. Simultaneously, it reduces the inter-variability of background color among the processed images. We argue that incorporating the color attributes and generative adversarial learning into a unified framework achieves high quality color normalization results. To the best of our knowledge, this is the first stain transfer method that incorporates the color attribute details with deep GAN network. Proposed SA-GAN is publicly available at: [[https://github.com/tasleem-hello/SA-GAN/tree/main](https://github.com/tasleem-hello/SA-GAN/tree/main)]([https://github.com/tasleem-hello/SA-GAN/tree/main](https://github.com/tasleem-hello/SA-GAN/tree/main)). The main contributions of this work are summarized as follows:  
* In this paper, color accumulation task is formulated as an unpaired image-to-image translation task. We aim to modify the color patterns of training data similar to the test images, without changing their structural details. * We propose a novel stain acclimation network named SA-GAN, with one generator and two discriminators which are trained with two adversarial losses and one textural loss in adversarial and transfer training steps, respectively. * The designed SA-GAN network is jointly trained with input training images and a novel metric called color attribute constraint. Incorporating the color attribute constraint and generative adversarial learning into a unified framework, correctly transfers the color contents and creates visually realistic images.