F2FLDM: Latent Diffusion Models with Histopathology Pre-Trained Embeddings for Unpaired Frozen Section to FFPE Translation

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Man Minh Ho, Shikha Dubey, Yosep Chong, Beatrice Knudsen, Tolga Tasdizen

Published: 2025, Last Modified: 17 Nov 2025WACV 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Frozen Section (FS) technique is a rapid method, taking only 15–30 minutes to prepare slides for pathologists' evaluation during surgery, enabling immediate surgical decisions. However, the FS process often introduces artifacts and distortions like folds and ice-crystal effects. In contrast, these artifacts are absent in higher-quality formalin-fixed paraffin-embedded (FFPE) slides, which take 2–3 days to prepare. Generative Adversarial Network (GAN)-based methods have been used to translate FS to FFPE images, but they may leave morphological inaccuracies or introduce new artifacts, reducing translation quality for clinical assessments. In this study, we benchmark recent generative models, focusing on GANs and Latent Diffusion Models (LDMs), to address these limitations. We introduce a novel approach combining LDMs with Histopathology Pre-Trained Embeddings11This research was supported by Population Science Award [DoD] (W81XWH-21-0725), Data Science Award [DoD] (HT94252410186), and VA Merit Award (1101CX002622-01). to enhance FS image restoration. Our framework uses LDMs conditioned by both text and pretrained embeddings to learn meaningful features of FS and FFPE images. Through diffusion and denoising processes, our approach preserves essential diagnostic attributes like color staining and tissue morphology using DDIM Inversion with FS representation. Additionally, it enhances the histological quality of translated images by predicting the targeted FFPE representation via an innovative embedding translation mechanism, improving morphological details and reducing FS artifacts. As a result, this work significantly improves classification performance, with the Area Under the Curve rising from 81.99% to 94.64%, and achieves the best results in user study. This work establishes a new benchmark for FS to FFPE image translation quality, promising enhanced reliability and accuracy in histopathology FS image analysis. Our work is available at https://minhmanho.github.io/f2f_ldm/.