## 2 Related Work
### Color Normalization  
CN is the most used technique to deal with color variation [19]. CN can standardize images by referencing an image and simulating a chosen staining procedure. CN methods normalize images with different techniques, including histogram matching, color transfer, and spectral matching [19]. Histogram matching disregards stain separation, color transfer modifies colors based on statistical correspondences between histological regions, and spectral matching estimates stain concentrations and color properties [31]. Among a variety of CN techniques, some are more popular, such as those proposed in [32; 33]. [PERSON] et al. [32] introduced the Mac technique (the proposed method in [32] is named Mac in this paper) in 2009, assuming that the amount of protein or nucleic acids is a random variable. Mac utilizes Singular Value Decomposition (SVD) to separate H&E channels. Within this technique, the concentration intensity of both the source and target images is scaled using the 99 th percentile to achieve a robust estimation of the maximum. [PERSON] et al. [33] published the Vah technique (the proposed method in [33] is named Vah in this paper) in 2016 to model the physical phenomena that define tissue structures. In this technique, there is a preferable stain color, based on pathologists' opinion, and a stain density map. Using an unsupervised approach, this method decomposes images into stain density maps. When Vah is applied to a specific image, it combines the relevant stain density maps based on a pathologist's preference for stain color. This process selectively modifies the color while preserving the underlying structure as described by the maps. The most recent techniques in CN are the auto encoders (AEs), generative adversarial networks (GANs), and Bayesian K-Singular Value Decomposition (BKSVD) [34; 35; 36]. [PERSON] et al. [34] applied a GAN to combine color normalization and the classification of WSIs. In this method, a generator is employed as a stain transfer network, while a discriminator separates the classes and the original and normalized images. In order to map unpaired images between two scanners, the cycleGAN was used by StainGAN [31]. In [35], the authors used three different convolutional neural network (CNN) models for CN purposes: the variational auto encoder (VAE), a GAN, and the deep convolutional Gaussian mixture model (DCGMM). In [37], a CN network is fed by a heavily augmented data set and trained to reconstruct the original appearance of WSIs. The Pix2 pix conditional GAN framework and CycleGAN are other notable CNN architectures. BKSVD [36], a cutting-edge technique that was proposed in 2022, utilizes an unsupervised estimation of the stain concentration that preserves histological structures with variational and empirical Bayes. In this paper, we provide a deep understanding of the effects of CN on the extracted features in the proposed CBHIR tool. Our experiments were conducted by applying three CN techniques in the pre-processing step in order to explore how CN influences the extracted features within the CBHIR tool. Among the CN techniques, Mac [32] and Vah [33] were selected as the two most used CN techniques and BKSVD was applied as a recent CN technique. Furthermore, in this work, we applied an unsupervised FE to tackle the need for a pool of histopathological images to train the deep learning model. The main contributions of this paper are as follows:  
1. Proposal of a new CBHIR framework with an unsupervised feature extractor that includes color normalization as a pre-processing step;
2. Analysis of CBHIR's performance when using normalized images in comparison with original images;
3. We draw attention to the relevance of color variation and its impact on CBHIR;
4. We provide a comprehensive performance assessment of the proposed method. This evaluation employs a large breast cancer database scored from five distinct laboratories.