颜色反卷积在HER2组织病理图像域自适应中的应用
Color Deconvolution applied to Domain Adaptation in HER2 histopathological images
AI总结：
本文提出结合颜色反卷积与Pix2Pix GAN的方法，解决HER2组织病理图像颜色归一化问题，在保持细胞类别和HER2评分方面优于现有风格迁移方法。
摘要：
乳腺癌的早期检测对于改善患者预后至关重要。加泰罗尼亚健康研究所（ICS）启动了DigiPatICS项目，旨在开发并实施人工智能算法以辅助癌症诊断。在本文中，我们提出了一种新方法来解决乳腺癌组织HER2染色组织病理图像中的颜色归一化问题，并将其视为风格迁移问题。我们将颜色反卷积技术与Pix2Pix GAN网络相结合，提出了一种新颖的方法来纠正不同HER2染色品牌之间的颜色差异。我们的方法专注于保持变换后图像中细胞的HER2评分，这对于HER2分析至关重要。结果表明，我们的最终模型在保持变换后图像中的细胞类别方面优于最先进的图像风格迁移方法，并且在生成逼真图像方面与它们同样有效。
英文摘要：
Breast cancer early detection is crucial for improving patient outcomes. The Institut Català de la Salut (ICS) has launched the DigiPatICS project to develop and implement artificial intelligence algorithms to assist with the diagnosis of cancer. In this paper, we propose a new approach for facing the color normalization problem in HER2-stained histopathological images of breast cancer tissue, posed as an style transfer problem. We combine the Color Deconvolution technique with the Pix2Pix GAN network to present a novel approach to correct the color variations between different HER2 stain brands. Our approach focuses on maintaining the HER2 score of the cells in the transformed images, which is crucial for the HER2 analysis. Results demonstrate that our final model outperforms the state-of-the-art image style transfer methods in maintaining the cell classes in the transformed images and is as effective as them in generating realistic images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
