Pushing the Limits of Cell Segmentation Models for Imaging Mass Cytometry
Author:
Affiliation:
1. University of Lincoln,School of Computer Science,Lincoln,U.K.
2. AstraZeneca Computational Pathology GmbH,Computer Vision & AI,Munich,Germany
Funder
Engineering and Physical Sciences Research Council
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635782.pdf?arnumber=10635782
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1. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
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3. A new mouse model of radiation-induced liver disease reveals mitochondrial dysfunction as an underlying fibrotic stimulus
4. Profiling of syngeneic mouse HCC tumor models as a framework to understand anti–PD‐1 sensitive tumor microenvironments
5. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
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