Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
This addresses a specific technical bottleneck for digital pathology applications where tilewise analysis is necessary due to high-resolution images.
The paper tackles the tiling artifact problem in whole slide image reconstruction for digital pathology style transfer, introducing a perceptual embedding consistency loss that significantly reduces artifacts while maintaining robustness to contrast, color, and brightness perturbations.
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.