Breast cancer early detection is crucial for improving patient outcomes. The
Institut Catal\`a 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.

该研究开发了一种新方法，使用人工智能算法解决乳腺癌组织病理学图像 HER2 染色的颜色标准化问题，该方法将颜色分离方法与 Pix2Pix GAN 网络相结合，以保持细胞的 HER2 评分，并在维持细胞类别的同时生成逼真的图像。

颜色去卷积在 HER2 组织病理图像域适应中的应用

Color Deconvolution applied to Domain Adaptation in HER2  histopathological images

The recognition of color texture under varying lighting conditions is still
an open issue. Several features have been proposed for this purpose, ranging
from traditional statistical descriptors to features extracted with neural
networks. Still, it is not completely clear under what circumstances a feature
performs better than the others. In this paper we report an extensive
comparison of old and new texture features, with and without a color
normalization step, with a particular focus on how they are affected by small
and large variation in the lighting conditions. The evaluation is performed on
a new texture database including 68 samples of raw food acquired under 46
conditions that present single and combined variations of light color,
direction and intensity. The database allows to systematically investigate the
robustness of texture descriptors across a large range of variations of imaging
conditions.

本文主要研究基于不同光线条件下的颜色纹理识别技术，通过对新旧特征及彩色规范化等方面进行广泛比较评估，研究不同的光照变化对纹理描述符的影响，得出在大范围成像条件下纹理描述符的鲁棒性。

在大量控制的光照条件下评估颜色纹理描述符

Evaluating color texture descriptors under large variations of  controlled lighting conditions

This article introduces a generalization of the discrete optimal transport,
with applications to color image manipulations. This new formulation includes a
relaxation of the mass conservation constraint and a regularization term. These
two features are crucial for image processing tasks, which necessitate to take
into account families of multimodal histograms, with large mass variation
across modes.
The corresponding relaxed and regularized transportation problem is the
solution of a convex optimization problem. Depending on the regularization
used, this minimization can be solved using standard linear programming methods
or first order proximal splitting schemes.
The resulting transportation plan can be used as a color transfer map, which
is robust to mass variation across images color palettes. Furthermore, the
regularization of the transport plan helps to remove colorization artifacts due
to noise amplification.
We also extend this framework to the computation of barycenters of
distributions. The barycenter is the solution of an optimization problem, which
is separately convex with respect to the barycenter and the transportation
plans, but not jointly convex. A block coordinate descent scheme converges to a
stationary point of the energy. We show that the resulting algorithm can be
used for color normalization across several images. The relaxed and regularized
barycenter defines a common color palette for those images. Applying color
transfer toward this average palette performs a color normalization of the
input images.

本文介绍了一种离散最优传输的推广，应用于彩色图像处理，并将其扩展到 distribution 的 Barycenters 计算，它们的混合体是通过图像彩色调整用于颜色归一化。