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- W4200375999 abstract "In modern clinical diagnosis, nuclei is an important basis for obtaining disease information, and rapid and accurate nuclei segmentation technology can effectively help doctors diagnose diseases. However, the difference in staining equipment or organs will cause staining differences between histopathological images, which will affect the effect of nuclei segmentation. Aiming at the problem of staining differences, we propose a cross-staining style nuclei pathology image synthesis and segmentation method based on adversarial learning. The algorithm is composed of a synthetic network and a segmentation network. The synthetic network adds a content discriminator to the cycle generative adversarial networks. Used to generate an image with the same staining style as the target domain, but the same content. The segmentation network segmented the generated image to verify the necessity of staining normalization and the performance of synthesis. In addition, the synthetic network also uses content loss function and style loss function to optimize network training. Experiments show that we propose a cross-staining style nuclei pathology image synthesis and segmentation method based on adversarial learning can effectively solve the problem of staining differences and optimize the effect of segmentation network." @default.
- W4200375999 created "2021-12-31" @default.
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- W4200375999 date "2021-09-01" @default.
- W4200375999 modified "2023-10-18" @default.
- W4200375999 title "Synthesis and Segmentation Method of Cross-Staining Style Nuclei Pathology Image Based on Adversarial Learning" @default.
- W4200375999 doi "https://doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom52081.2021.00078" @default.
- W4200375999 hasPublicationYear "2021" @default.
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