MIA: David Van Valen, Deep learning & multiplexed images; Primer by Emily Laubscher
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Models, Inference and Algorithms
Broad Institute of MIT and Harvard
March 3, 2021
Meeting: Single-cell biology in a software 2.0 world
David Van Valen
Division of Biology and Biological Engineering, California Institute of Technology
Multiplexed imaging methods can measure the expression of dozens of proteins while preserving spatial information. While these methods open an exciting new window into the biology of human tissues, interpreting the images they generate with single cell resolution remains a significant challenge. Current approaches to this problem in tissues rely on identifying cell nuclei, which results in inaccurate estimates of cellular phenotype and morphology. In this work, we overcome this limitation by combining multiplexed imaging’s ability to image nuclear and membrane markers with large-scale data annotation and deep learning. We describe the construction of TissueNet, an image dataset containing more than one million paired whole-cell and nuclear annotation