# Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention

arXiv: https://arxiv.org/abs/2411.06583 · HTML: https://mlsanity.com/paper/2411.06583/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 10 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation](https://mlsanity.com/paper/2211.12572/) (2022)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [Cell Detection with Star-convex Polygons](https://mlsanity.com/paper/1806.03535/) (2018)
- [Pretraining is All You Need for Image-to-Image Translation](https://mlsanity.com/paper/2205.12952/) (2022)
- [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019)
- [Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN](https://mlsanity.com/paper/1809.04536/) (2018)

## Built on by (most cited first, 1 of 1)

- [PRISM: Distribution-Gated Flow Matching for Controllable Unpaired Image Translation](https://mlsanity.com/paper/2608.06240/) (2026)

## Abstract (arXiv)

In histological pathology, frozen sections are often used for rapid diagnosis during surgeries, as they can be produced within minutes. However, they suffer from artifacts and often lack crucial diagnostic details, particularly within the cell nuclei region. Permanent sections, on the other hand, contain more diagnostic detail but require a time-intensive preparation process. Here, we present a generative deep learning approach to enhance frozen section images by leveraging guidance from permanent sections. Our method places a strong emphasis on the nuclei region, which contains critical information in both frozen and permanent sections. Importantly, our approach avoids generating artificial data in blank regions, ensuring that the network only enhances existing features without introducing potentially unreliable information. We achieve this through a segmented attention network, incorporating nuclei-segmented images during training and adding an additional loss function to refine the nuclei details in the generated permanent images. We validated our method across various tissues, including kidney, breast, and colon. This approach significantly improves histological efficiency and diagnostic accuracy, enhancing frozen section images within seconds, and seamlessly integrating into existing laboratory workflows.