Abstract
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.
Abstract (translated)
乳腺癌早期检测对于改善患者结果至关重要。西班牙卫生研究所(ICS)启动了DigiPatICS项目，旨在开发和实施人工智能算法，以协助乳腺癌的诊断。在本文中，我们提出了一种新的方法来解决乳腺癌组织中HER2染色后色彩正则化问题，将其视为风格转移问题。我们结合了色彩复原技术和Pix2PixGAN网络，提出了一种新方法来纠正不同HER2染色品牌之间的色彩差异。我们的 approach 重点是维持转换后图像中的细胞HER2得分，这对于HER2分析至关重要。结果证明，我们的最终模型在维持转换后图像中的细胞类别方面比最先进的图像风格转移方法更有效，并且生成真实图像的效果也类似。
URL
https://arxiv.org/abs/2305.07404
