A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasting 12–48 h) and hence unsuitable for intra-operative use. Here we report the development and performance of a deep-learning model that improves the quality of cryosectioned whole-slide images by transforming them into the style of whole-slide FFPE tissue within minutes. The model consists of a generative adversarial network incorporating an attention mechanism that rectifies cryosection artefacts and a self-regularization constraint between the cryosectioned and FFPE images for the preservation of clinically relevant features. Transformed FFPE-style images of gliomas and of non-small-cell lung cancers from a dataset independent from that used to train the model improved the rates of accurate tumour subtyping by pathologists. © 2022, The Author(s), under exclusive licence to Springer Nature Limited.M.T., K.B.O. and G.I.G. are grateful to the Scientific and Technological Research Council of Turkey (TUBITAK) for a 2232 International Outstanding Researcher Fellowship and to TUBITAK Ulakbim for the Turkish National e-Science e-Infrastructure (TRUBA)-cluster and data-storage services. We also thank Ö. Asar and H. Okut for their guidance and assistance in evaluation of the results.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTA