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Context-Based Normalization of Histological Stains Using Deep Convolutional Features

机译：使用深卷积特征的组织学污渍的基于背景基础规范化

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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.
机译：虽然人类观察者能够应对组织学污渍的颜色和外观的变化，但数字病理算法通常需要良好的归一化设置来实现峰值性能，尤其是当有限量的标记数据时，尤其是当可用数量的标记数据时。这项工作提供了一个全自动，基于端到端学习的基于基于学习的设置，用于归一化组织学污渍，其考虑组织的纹理背景。我们介绍了特征意识的标准化，其与来自长短期存储器单元的Gating元件组合扩展了批量归一化框架，以便在不同的空间区域之间的归一化。通过将掠过的深神经网络纳入特征提取器转向像素化处理管道，我们实现了出色的归一化结果，并确保了一致的颜色和纹理表示。评估包括颜色直方图偏差，结构相似性和测量通过不同方法获得的颜色体积的比较。

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