Ultra-high-resolution unpaired stain transformation via Kernelized Instance Normalization
Ming-Yang Ho*
Min-Sheng Wu
Che-Ming Wu


While hematoxylin and eosin (H&E) is a standard staining procedure, immunohistochemistry (IHC) staining further serves as a diagnostic and prognostic method. However, acquiring special staining results requires substantial costs. Hence, we proposed a strategy for ultra-high-resolution unpaired image-to-image translation: Kernelized Instance Normalization (KIN), which preserves local information and successfully achieves seamless stain transformation with constant GPU memory usage. Given a patch, corresponding position, and a kernel, KIN computes local statistics using convolution operation. In addition, KIN can be easily plugged into most currently developed frameworks without re-training. We demonstrate that KIN achieves state-of-the-art stain transformation by replacing instance normalization (IN) layers with KIN layers in three popular frameworks and testing on two histopathological datasets. Furthermore, we manifest the generalizability of KIN with high-resolution natural images. Finally, human evaluation and several objective metrics are used to compare the performance of different approaches. Overall, this is the first successful study for the ultra-high-resolution unpaired image-to-image translation with constant space complexity.


The implementation of KIN is fully provided in our official GitHub repository. To reproduce the experiments, please follow the steps described in the README.md file.


Paper and Supplementary Material

M.Y. Ho, M.S. Wu, C.M. Wu.
Ultra-high-resolution unpaired stain transformation via Kernelized Instance Normalization.
ECCV, 2022.
(hosted on ArXiv)



This template was originally made by Phillip Isola and Richard Zhang for a colorful ECCV project; the code can be found here.