  The Frozen Section (FS) technique is a rapid and efficient method, taking
only 15-30 minutes to prepare slides for pathologists' evaluation during
surgery, enabling immediate decisions on further surgical interventions.
However, FS process often introduces artifacts and distortions like folds and
ice-crystal effects. In contrast, these artifacts and distortions are absent in
the higher-quality formalin-fixed paraffin-embedded (FFPE) slides, which
require 2-3 days to prepare. While Generative Adversarial Network (GAN)-based
methods have been used to translate FS to FFPE images (F2F), they may leave
morphological inaccuracies with remaining FS artifacts or introduce new
artifacts, reducing the quality of these translations for clinical assessments.
In this study, we benchmark recent generative models, focusing on GANs and
Latent Diffusion Models (LDMs), to overcome these limitations. We introduce a
novel approach that combines LDMs with Histopathology Pre-Trained Embeddings to
enhance restoration of FS images. Our framework leverages LDMs conditioned by
both text and pre-trained embeddings to learn meaningful features of FS and
FFPE histopathology images. Through diffusion and denoising techniques, our
approach not only preserves essential diagnostic attributes like color staining
and tissue morphology but also proposes an embedding translation mechanism to
better predict the targeted FFPE representation of input FS images. As a
result, this work achieves a significant improvement in classification
performance, with the Area Under the Curve rising from 81.99% to 94.64%,
accompanied by an advantageous CaseFD. 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/.
