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In contrast to paraffin sections, frozen sections can be quickly generatedduring surgical interventions. This procedure allows surgeons to wait forhistological findings during the intervention to base intra-operative decisionson the outcome of the histology. However, compared to paraffin sections, thequality of frozen sections is typically lower, leading to a higher ratio ofmiss-classification. In this work, we investigated the effect of the sectiontype on automated decision support approaches for classification of thyroidcancer. This was enabled by a data set consisting of pairs of sections forindividual patients. Moreover, we investigated, whether a frozen-to-paraffintranslation could help to optimize classification scores. Finally, we propose aspecific data augmentation strategy to deal with a small amount of trainingdata and to increase classification accuracy even further.

Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks