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Semantic filtering through deep source separation on microscopy images

Abstract:

By their very nature microscopy images of cells and tissues consist of a limited number of object types or components. In contrast to most natural scenes, the composition is known a priori. Decomposing biological images into semantically meaningful objects and layers is the aim of this paper. Building on recent approaches to image de-noising we present a framework that achieves state-of-the-art segmentation results requiring little or no manual annotations. Here, synthetic images generated by...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-32692-0_57

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Oxford Ludwig Institute
Role:
Author
Publisher:
Springer Publisher's website
Journal:
International Workshop on Machine Learning in Medical Imaging Journal website
Volume:
11861
Pages:
498-506
Series:
Lecture Notes in Computer Science
Host title:
Machine Learning in Medical Imaging
Publication date:
2019-10-10
Acceptance date:
2019-08-13
DOI:
ISSN:
0302-9743
Source identifiers:
1054387
ISBN:
9783030326920
Keywords:
Pubs id:
pubs:1054387
UUID:
uuid:4b4f2a6e-ef5b-4bc4-a6b7-600cf21abe1e
Local pid:
pubs:1054387
Deposit date:
2019-10-27

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