Stain normalization is a crucial pre-processing step for histopathological image processing, and can help improve the accuracy of downstream tasks such as segmentation and classification. To evaluate the effectiveness of stain normalization methods, various metrics based on color-perceptual similarity and stain color evaluation have been proposed. However, there still exists a huge gap between metric evaluation and human perception, given the limited explainability power of existing metrics and inability to combine color and semantic information efficiently. Inspired by the effectiveness of deep neural networks in evaluating perceptual similarity of natural images, in this paper, we propose TriNet-P, a color-perceptual similarity metric for whole slide images, based on deep metric embeddings. We evaluate the proposed approach using four publicly available breast cancer histological datasets. The benefit of our approach is its representation efficiency of the perceptual factors associated with H&E stained images with minimal human intervention. We show that our metric can capture the semantic similarities, both at subject (patient) and laboratory levels, and leads to better performance in image retrieval and clustering tasks.
Keywords: Metric Embedding; Perceptual Similarity; Representation Learning; Whole-Slide Imaging.