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Bayesian fluorescence in situ hybridisation signal classification
Boaz Lerner
Department of Electrical & Computer Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
29
Scopus citations
Overview
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Dive into the research topics of 'Bayesian fluorescence in situ hybridisation signal classification'. Together they form a unique fingerprint.
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Engineering & Materials Science
Fluorescence
100%
Classifiers
61%
Neural networks
32%
Covariance matrix
22%
Fluorophores
18%
Network performance
10%
Decomposition
7%
Medicine & Life Sciences
Gm(m)
95%
Fluorescence In Situ Hybridization
88%
Spatial Analysis
22%
Artifacts
16%