H&E Multi-Laboratory Staining Variance Exploration with Machine Learning

In diagnostic histopathology, hematoxylin and eosin (H&E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although method does not change. This variance can impair accuracy algorithms histopathologists’ time-to-insight. Investigating this help calibrate stain normalization tasks to reverse negative potential. With machine learning, study evaluated different on three tissue types. We received H&E-stained slides from 66 laboratories. Each slide contained kidney, skin, colon samples stained by routinely used in each laboratory. The were digitized summarized as red, green, blue channel histograms. Dimensions reduced using principal component analysis. data projected components inserted into k-means clustering algorithm k-nearest neighbors classifier with target. silhouette index indicated K = 2 clusters had best separability all supervised classification result showed laboratory effects tissue-type bias. Both unsupervised approaches suggested type also affected inter-laboratory variance. suggest be considered upon choosing color-normalization approach.