Even after more than a hundred years after its introduction, histology remains the gold standard in tumor diagnosis and prognosis. Anatomic pathologists evaluate histology to stratify cancer patients into different groups depending on their tumor genotypes and phenotypes, and their clinical outcome [1,2]. However, human evaluation of histological slides is subjective and not repeatable [3]. Furthermore, histological assessment is a time-consuming process that requires highly trained professionals.
With significant technological advances in the last decade, techniques such as whole slide imaging (WSI) and deep learning (DL) are now widely available. WSI is the scanning of conventional microscopy glass slides to produce a single, high-resolution image from those slides. This allows for the digitization and collection of large sets of pathology images, which would have been prohibitively time-consuming and expensive. The availability of such datasets creates new and innovative ways of accelerating diagnosis by using techniques such as machine learning (ML)

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