ArticleProceedings of machine learning research2023
Unsupervised Stain Decomposition via Inversion Regulation for Multiplex Immunohistochemistry Images.
Article in Proceedings of machine learning research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Papanicolaou stain unmixing for RGB image using weighted nucleus sparsity and total variation regularization.Medical & biological engineering & computing · 2026Article
- Label-Efficient Deep Color Deconvolution of Brightfield Multiplex IHC Images.IEEE transactions on medical imaging · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Multiplex Immunohistochemistry (mIHC) is a cost-effective and accessible method for in situ labeling of multiple protein biomarkers in a tissue sample. By assigning a different stain to each biomarker, it allows the visualization of different types of cells within the tumor vicinity for downstream analysis. However, to detect different types of stains in a given mIHC image is a challenging problem, especially when the number of stains is high. Previous deep-learning-based methods mostly assume full supervision; yet the annotation can be costly. In this paper, we propose a novel unsupervised stain decomposition method to detect different stains simultaneously. Our method does not require any supervision, except for color samples of different stains. A main technical challenge is that the problem is underdetermined and can have multiple solutions. To conquer this issue, we propose a novel inversion regulation technique, which eliminates most undesirable solutions. On a 7-plexed IHC images dataset, the proposed method achieves high quality stain decomposition results without human annotation.
Indexed as
Identifiers
38817539PMC11138139What OpenQuestion holds
Registered trials
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