Abstract
Multiplexed pathology imaging techniques allow spatially resolved analysis of cell phenotypes for interrogating disease biology. Existing methods for cell phenotyping in multiplex images require extensive annotation workload due to the need for fully supervised training. To overcome this challenge, we develop SANDI, a self-supervised-based pipeline that learns intrinsic similarities in unlabeled cell images to mitigate the requirement for expert supervision. The capability of SANDI to efficiently classify cells with minimal manual annotations is demonstrated through the analysis of 3 different multiplexed immunohistochemistry datasets. We show that in coupled with representations learnt by SANDI from unlabeled cell images, a linear Support Vector Machine classifier trained on 10 annotations per cell type yields a higher or comparable weighted F1-score to the supervised classifier trained on an average of about 300–1000 annotations per cell type. By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for multiplexed imaging data.
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Zhang, H. et al. (2022). Self-supervised Antigen Detection Artificial Intelligence (SANDI). In: Xu, X., Li, X., Mahapatra, D., Cheng, L., Petitjean, C., Fu, H. (eds) Resource-Efficient Medical Image Analysis. REMIA 2022. Lecture Notes in Computer Science, vol 13543. Springer, Cham. https://doi.org/10.1007/978-3-031-16876-5_2
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DOI: https://doi.org/10.1007/978-3-031-16876-5_2
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