A Fast Texture-to-Stain Adversarial Stain Normalization Network for Histopathological Images.

Histopathological images, as the gold standard for cancer diagnosis, record abundant information about microscopic structures and morphological characteristics through staining and scanning. However, the stain variation caused by the deviations in the production process not only confuse the pathological diagnosis, but also depress the generalization of computer-aided diagnosis. Stain normalization provides an efficient preprocessing method, which generates images with the same stain style while preserving the texture information. In this paper, we extend the advantages of adversarial transfer learning and auto-encoder structure to propose an adversarial stain normalization network (ASNN). For the stain style alignment, ASNN liberalizes the selection for the specific template image and adopts a domain discriminator after encoding to eliminate the distribution discrepancies of latent features caused by stain variation. To ensure precisely stain style transfer, we reconstruct the target stain style image using a reconstruction loss-constrained autoencoder and by the loss of reconstruction so that the stain decoder learns the rules for texture-to-stain conversions. We simulate the experiments on two publicly available datasets and utilize multiple indicators to evaluate the qualities of generated images. By comparing classification performance on downstream tasks, ASNN exhibits its superiority than traditional stain normalization methods. Furthermore, ASNN adopts a lighter network structure, which is faster than traditional stain normalization methods, and can stain 500 images within 1 second.