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

TL;DR

This paper explores using generative adversarial networks to translate frozen histological sections into paraffin-like images, aiming to improve thyroid cancer classification accuracy during surgery.

Contribution

It introduces a frozen-to-paraffin translation method and a data augmentation strategy to enhance automated classification of thyroid cancer from frozen sections.

Findings

Frozen-to-paraffin translation improves classification accuracy.

Data augmentation further enhances model performance.

The approach aids intra-operative decision-making.

Abstract

In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during the intervention to base intra-operative decisions on the outcome of the histology. However, compared to paraffin sections, the quality of frozen sections is typically lower, leading to a higher ratio of miss-classification. In this work, we investigated the effect of the section type on automated decision support approaches for classification of thyroid cancer. This was enabled by a data set consisting of pairs of sections for individual patients. Moreover, we investigated, whether a frozen-to-paraffin translation could help to optimize classification scores. Finally, we propose a specific data augmentation strategy to deal with a small amount of training data and to increase classification accuracy even further.