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Improving whole slide segmentation through visual context: a systematic study

Abstract:

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation perf...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-00934-2_22

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
Surgical Sciences
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
National Institute for Health Research More from this funder
Publisher:
Springer, Cham Publisher's website
Journal:
International Conference On Medical Image Computing & Computer Assisted Intervention (MICCAI) 2018 Journal website
Volume:
11071
Pages:
192-200
Series:
Lecture Notes in Computer Science
Host title:
MICCAI 2018: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Publication date:
2018-09-26
Acceptance date:
2018-05-30
DOI:
ISSN:
0302-9743
Source identifiers:
854803
ISBN:
9783030009342
Keywords:
Pubs id:
pubs:854803
UUID:
uuid:5fa5653e-4979-4383-b650-1b3ee0e80ecd
Local pid:
pubs:854803
Deposit date:
2018-06-04

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