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Unsupervised Stain Decomposition via Inversion Regulation for Multiplex Immunohistochemistry Images |
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 image dataset, the proposed method achieves high quality stain decomposition results without human annotation. |
inproceedings |
Proceedings of Machine Learning Research |
PMLR |
2640-3498 |
abousamra24a |
0 |
Unsupervised Stain Decomposition via Inversion Regulation for Multiplex Immunohistochemistry Images |
74 |
94 |
74-94 |
74 |
false |
Abousamra, Shahira and Fassler, Danielle and Yao, Jiachen and Gupta, Rajarsi R. and Kurc, Tahsin and Escobar-Hoyos, Luisa and Samaras, Dimitris and Shroyer, Kenneth and Saltz, Joel and Chen, Chao |
|
2024-01-23 |
Medical Imaging with Deep Learning |
227 |
inproceedings |
|