1 research outputs found
Multi-stage domain adversarial style reconstruction for cytopathological image stain normalization
The different stain styles of cytopathological images have a negative effect
on the generalization ability of automated image analysis algorithms. This
article proposes a new framework that normalizes the stain style for
cytopathological images through a stain removal module and a multi-stage domain
adversarial style reconstruction module. We convert colorful images into
grayscale images with a color-encoding mask. Using the mask, reconstructed
images retain their basic color without red and blue mixing, which is important
for cytopathological image interpretation. The style reconstruction module
consists of per-pixel regression with intradomain adversarial learning,
inter-domain adversarial learning, and optional task-based refining. Per-pixel
regression with intradomain adversarial learning establishes the generative
network from the decolorized input to the reconstructed output. The interdomain
adversarial learning further reduces the difference in stain style. The
generation network can be optimized by combining it with the task network.
Experimental results show that the proposed techniques help to optimize the
generation network. The average accuracy increases from 75.41% to 84.79% after
the intra-domain adversarial learning, and to 87.00% after interdomain
adversarial learning. Under the guidance of the task network, the average
accuracy rate reaches 89.58%. The proposed method achieves unsupervised stain
normalization of cytopathological images, while preserving the cell structure,
texture structure, and cell color properties of the image. This method
overcomes the problem of generalizing the task models between different stain
styles of cytopathological images