# H&E Multi-Laboratory Staining Variance Exploration with Machine Learning
## 1 Introduction  
Contemporary digital workflows have overtaken histopathology's traditional diagnostic and analysis protocols [1]. It is standard for pathologists to use digitalized images within virtual microscope software. Such software visualizes images obtained from a scanning microscope. Typically, an image consists of an entire glass slide of tissue stained to highlight relevant biological features. A microscope illuminates a given slide and allows the digitization of the sample. Any given staining approach has affiliated interactions with specific biological components in the slide [2]. If a histological slide is not stained, it remains transparent and will be masked by the illuminance source of the microscope penetrating through the tissue. In a standard, red, green, and blue (RGB) channel format, the stain will absorb a distinct amount of light in each channel [2]. The resulting images will thus vary due to laboratory changes in stain manufacturer, general storing conditions, microscope/slide scanner configuration, and dye-application methods [1; 3; 4; 2]. In diagnostic applications, hematoxylin and eosin (H&E) is the default staining method for revealing salient histologic features [5]. Hematoxylin stains histologic cell nuclei toa purple-blue hue, while eosin stains cytoplasm and extracellular matrixes to a pink-red hue. Like other staining methods, the results of an H&E stain may vary between different laboratories or even within the same laboratory [2]. Such variance negatively impacts the time-to-insight for histopathologys and can even affect the accuracy of computer-aided diagnostic software (CAD) [4; 6; 7; 8]. It is therefore imperative to effectively normalize the staining variance between histological slides. Various methods have been developed for stain normalization [2; 9; 10; 11; 12; 13; 14]. Often, methods require a target image to imitate by artificially altering an input image [15; 16]. Essentially, the staining variance of one laboratory is used to alter out-of-laboratory stain samples. Studies such as [17] highlight the efficacy of structure-preserving normalization [18] with a target. In work [19], it was noted that the Food and Drugs Administration recommended adopting target-based normalization. Overall, the variance between laboratories can be essential in organizing available targets for more effective normalization. As noted in [19], there is no 'gold standard' when it comes to color-management issues. Regarding classification effects, prior investigations show that color normalization within samples of the same laboratory did not positively affect classification accuracy [20]. In addition, the same study showed inconsistent positive effects across all its datasets. Another study [21] showed that color pre-processing reduced accuracy in cancer classification, especially when coupled with color related features. In that regard, another work showed similar effects for kidney tissue classification [22]. In [8], the authors showed that color transformations were more effective than color normalization in four different applications. In contrast positive accuracy effects of color normalization were previously shown for diagnostic tasks such as colorectal cancer classification [6]. All these results strongly highlighted a potential task-related positive or negative impact of color normalization approaches. We saw that stain normalization approaches have been investigated in various ways. To date, the underlying structure of inter-laboratory stain variance as a normalization target remains unclear. How can it be systematically exploited for task specific positive normalization effects? When considering this variance, we first consider the following questions. How many laboratories have distinct staining results? Are staining effects consistent between tissue types?