  Stain normalization often refers to transferring the color distribution of
the source image to that of the target image and has been widely used in
biomedical image analysis. The conventional stain normalization is regarded as
constructing a pixel-by-pixel color mapping model, which only depends on one
reference image, and can not accurately achieve the style transformation
between image datasets. In principle, this style transformation can be well
solved by the deep learning-based methods due to its complicated network
structure, whereas, its complicated structure results in the low computational
efficiency and artifacts in the style transformation, which has restricted the
practical application. Here, we use distillation learning to reduce the
complexity of deep learning methods and a fast and robust network called
StainNet to learn the color mapping between the source image and target image.
StainNet can learn the color mapping relationship from a whole dataset and
adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner
restricts the network size and avoids artifacts in the style transformation.
The results on the cytopathology and histopathology datasets show that StainNet
can achieve comparable performance to the deep learning-based methods.
Computation results demonstrate StainNet is more than 40 times faster than
StainGAN and can normalize a 100,000x100,000 whole slide image in 40 seconds.
