Semi-automated annotation of cell type-specific protein expression patterns in human testis based on immunohistochemistry

Abstract Immunohistochemistry (IHC) provides the basis for cell type-specific localization of protein expression patterns in human tissues. Manual annotation of complex IHC images is however expensive and may lead to errors or inter-observer variability. Artificial Intelligence holds much promise for efficient and accurate pattern recognition, but confidence in prediction needs to be addressed. To present a reliable model for annotation of IHC images, we developed a semi-automated framework for multi-label classification of 7,848 human testis samples stained with IHC, and manually annotated in situ protein expression in eight different cell types. The dataset was used as a basis for training and testing a proposed Hybrid Bayesian Neural Network. By combining the deep learning model with a novel uncertainty metric, the average diagnostic performance improved from 86.9% to 96.3%. The streamlined workflow has important implications for accurate large-scale efforts mapping the human cell type-specific proteome in health and disease.