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Confidence and Uncertainty - A multilabel AI-based model for evaluating protein expression in testis

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In a study led by researchers in the HPA and at Brunel University London, a novel method for automated annotation of immunohistochemistry images was developed for annotating cell type-specific protein expression of 8 different cell types in human testis. The work comprised 7848 images (corresponding to 2794 proteins) and the image classifier also provided a novel uncertainty metrics (called DeepHistoClass), for identification of manual annotation errors. The workflow can be implemented for other tissues or utilized in large-scale protein mapping efforts for sourcing high-quality data...Read more