3 research outputs found
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
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