Stain Normalization of Histopathology Images using conditional Generative Adversarial Networks (cGAN)

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Salehi Pegah; Chalechale Abdolah

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Journal Paper

Paper Information

Journal: MACHINE VISION AND IMAGE PROCESSING Year:2021 | Volume:8 | Issue:2 Page(s): 25-41

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Information Journal Paper

Title

Stain Normalization of Histopathology Images using conditional Generative Adversarial Networks (cGAN)

Author(s)

Salehi Pegah | Chalechale Abdolah | Issue Writer Certificate

Keywords

Deep Learning

conditional Generative Adversarial Network (cGAN)

Image-to-Image Translation

Histopathology Images

Stain Normalization

Abstract

The diagnosis of cancer is mainly performed by visual analysis of pathologists through examining the morphology of the tissue slices under a microscope. If the microscopic image of a specimen is not stained, it will look colorless and without texture. Therefore, chemical staining is required to create adequate contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, and types of illness, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to interpretive disparity among pathologists, is one of the main challenges in designing robust and flexible systems for automated analysis. Various strategies for Stain Normalization have been proposed as a pre-processing step in the pipeline of the automated systems. The pix2pix methodwhich is derived from the conditional Generative Adversarial Networks (cGAN) is one of the powerful methods for solving Image-to-Image Translation problems. The main innovation of this paper is to present a new powerful method for the Stain Normalization of Histopathology Images using the Pix2Pix method, which is implemented and evaluated on the Mitos-Atypia-14 dataset.In the proposed method, grayscale images are given as input to the network, and then the system learns to restain the texture of the input image in a specific coloring style by preserving the structure and corresponding histopathological pattern. This method, compared to previous methods that relied on a reference image, instead uses the distribution of all images in the learning phase. The proposed method has achieved significant resultsboth in quantitative and qualitative evaluations comparing to some well-known methods in the literature.Moreover, as another innovation, the proposed method tested in a clinical use-case, namely breast cancer tumor classification,using the PatchCamelyon datasetand itshowsa 5% increase in the AUC parameter.

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