# Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer

## TL;DR

This paper introduces a perceptual embedding consistency loss to reduce tiling artifacts in high-resolution digital pathology images reconstructed from tilewise style transfer, improving image quality and robustness.

## Contribution

The novel perceptual embedding consistency loss effectively minimizes tiling artifacts in whole slide image reconstruction from tilewise style transfer.

## Key findings

- Significantly reduces tiling artifacts in reconstructed images.
- Enhances robustness to contrast, color, and brightness variations.
- Achieves more realistic and consistent virtual staining results.

## Abstract

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.