ArticleIEEE transactions on medical imaging2026
Label-Efficient Deep Color Deconvolution of Brightfield Multiplex IHC Images.
Article in IEEE transactions on medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Brightfield Multiplex Immunohistochemistry (mIHC) provides simultaneous labeling of multiple protein biomarkers in the same tissue section. It enables the exploration of spatial relationships between the inflammatory microenvironment and tumor cells, and to uncover how tumor cell morphology relates to cancer biomarker expression. Color deconvolution is required to analyze and quantify the different cell phenotype populations present as indicated by the biomarkers. However, this becomes a challenging task as the number of multiplexed stains increase. In this work, we present self-supervised and semi-supervised approaches to mIHC color deconvolution. Our proposed methods are based on deep convolutional autoencoders and learn using innovative reconstruction losses inspired by physics. We show how we can integrate weak annotations and the abundant unlabeled data available to train a model to reliably unmix the multiplexed stains and generate stain segmentation maps. We demonstrate the effectiveness of our proposed methods through experiments on mIHC dataset of 7-plexed IHC images.
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