**8.6 Computational pathology**

Classical pathology consists, very briefly, of the preservation, treatment, and staining of very small portions of tissue in slides. Stains can be standard ones, which highlight general structures, such as nuclei or cytoplasm, or immunohistochemical stains, in which specific cellular markers are targeted [103]. Thanks to advances in storage capabilities and the availability of cloud computing, the last few years have seen a migration from direct microscopic observation of stained tissues to the digitization of slides. Digital slides are stored in a specific file type called whole slide image (WSI), where it is possible to store the different magnification planes with very high compression. The scanning of the slides and the production of WSI for different uses, such as telepathology, constitute a branch of pathology called digital pathology [30, 104]. In addition, the increasing production and cataloging of WSIs for the diagnosis of different diseases made it possible to use them as training and testing materials for computer vision algorithms. This application of algorithms in WSIs has been called computational pathology and most of the published works use deep learning as a basis for different tasks. In a very general manner, one could describe the process of creating a computational pathology pipeline for any disease. Once the WSIs of the pathology to be studied are available, the final magnification to work with must be selected (20, 40) and consecutive patches of the different zones (disease and healthy tissue) must be generated [30]. The patches are generated due to the large size of the WSIs (the highest magnification can exceed 3e10 pixels). Consequently, the patches are used as input to the model and the model will learn, according to the task, to identify tumor and non-tumor zones [30]. In test WSIs, the same technique can be used to generate patches, process them with the model and then reconstruct the final image with a heat map. The heat map will identify the regions with the highest probability of belonging to a class (healthy or tumor). Jiang et al. categorize the implementation of computational pathology in oncology into five purposes, which are tumor diagnosis, subtyping, grading, staging, and prognosis [30]. Thus, we can find applications of these five purposes for breast cancer [30, 105–108], lung cancer [30, 109–111], colorectal cancer [30, 112–115], gastric cancer [30, 116, 117], prostate

cancer [30, 118, 119], and thyroid cancer [30, 120, 121]. Another set of applications of computational pathology lies in the automatic analysis for the identification of rejection in organ transplantation. Several papers have been published for kidney [122, 123] and heart [124] transplantation.
