While human observers are able to cope with variations in color and
appearance of histological stains, digital pathology algorithms commonly
require a well-normalized setting to achieve peak performance, especially when
a limited amount of labeled data is available. This work provides a fully
automated, end-to-end learning-based setup for normalizing histological stains,
which considers the texture context of the tissue. We introduce Feature Aware
Normalization, which extends the framework of batch normalization in
combination with gating elements from Long Short-Term Memory units for
normalization among different spatial regions of interest. By incorporating a
pretrained deep neural network as a feature extractor steering a pixelwise
processing pipeline, we achieve excellent normalization results and ensure a
consistent representation of color and texture. The evaluation comprises a
comparison of color histogram deviations, structural similarity and measures
the color volume obtained by the different methods.

本研究提供了一种用于标准化组织学染色的完全自动化、端到端基于学习的设置，该设置考虑了组织的纹理上下文，并通过结合来自长短时记忆单元的门控元素的批量归一化框架来扩展特征感知归一化。为了实现优秀的标准化结果并确保颜色和纹理的一致表示，作者将预训练的深度神经网络作为特征提取器来引导像素处理管道，并使用颜色直方图偏差、结构相似性和颜色体积等指标对不同方法进行评估。