Context-based Normalization of Histological Stains using Deep Convolutional Features
This addresses the need for automated stain normalization in digital pathology, particularly when labeled data is limited, though it appears incremental as it builds on existing normalization frameworks.
The paper tackled the problem of color and appearance variations in histological stains that hinder digital pathology algorithms, by introducing Feature Aware Normalization, which achieved excellent normalization results with consistent color and texture representation.
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.