# Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer

arXiv: https://arxiv.org/abs/1906.00617 · HTML: https://mlsanity.com/paper/1906.00617/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss](https://mlsanity.com/paper/2204.09782/) (2022)
- [Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging](https://mlsanity.com/paper/2109.09004/) (2021)
- [ParamNet: A Dynamic Parameter Network for Fast Multi-to-One Stain Normalization](https://mlsanity.com/paper/2305.06511/) (2023)
- [SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling](https://mlsanity.com/paper/2607.18990/) (2026)

## Abstract (arXiv)

Style transfer is a field with growing interest and use cases in deep learning. Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images of virtually stained slide images in digital pathology with clinically validated interpretability. Digital pathology images are typically of extremely high resolution, making tilewise analysis necessary for deep learning applications. It has been shown that image generators with instance normalization can cause a tiling artifact when a large image is reconstructed from the tilewise analysis. We introduce a novel perceptual embedding consistency loss significantly reducing the tiling artifact created in the reconstructed whole slide image (WSI). We validate our results by comparing virtually stained slide images with consecutive real stained tissue slide images. We also demonstrate that our model is more robust to contrast, color and brightness perturbations by running comparative sensitivity analysis tests.