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Identifying nuclear phenotypes using semi-supervised metric learning

Abstract:

In systems-based approaches for studying processes such as cancer and development, identifying and characterizing individual cells within a tissue is the first step towards understanding the large-scale effects that emerge from the interactions between cells. To this end, nuclear morphology is an important phenotype to characterize the physiological and differentiated state of a cell. This study focuses on using nuclear morphology to identify cellular phenotypes in thick tissue sections image...

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Journal:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume:
6801 LNCS
Pages:
398-410
Publication date:
2011-01-01
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
URN:
uuid:cc538c0d-f760-4678-b770-9549e17ee919
Source identifiers:
439048
Local pid:
pubs:439048
Language:
English
Keywords:

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