Conference item
Microscopy cell counting with fully convolutional regression networks
- Abstract:
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This paper concerns automated cell counting in microscopy images. The approach we take is to adapt Convolutional Neural Networks (CNNs) to regress a cell spatial density map across the image. This is applicable to situations where traditional single-cell segmentation based methods do not work well due to cell clumping or overlap. We make the following contributions: (i) we develop and compare architectures for two Fully Convolutional Regression Networks (FCRNs) for this task; (ii) since the n...
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- Publication status:
- Not published
- Peer review status:
- Peer reviewed
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Funding
Bibliographic Details
- Publisher:
- TUM Technische Universität München Publisher's website
- Host title:
- 18th International Conference on Medical Image Computing and Computer Analysis Interventions (MICCAI 2015)
- Publication date:
- 2015-01-01
- Acceptance date:
- 2015-02-02
Item Description
- Pubs id:
-
pubs:678982
- UUID:
-
uuid:3ffa263f-48d7-4479-8a11-8d0edc07793c
- Local pid:
- pubs:678982
- Source identifiers:
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678982
- Deposit date:
- 2017-02-09
Terms of use
- Copyright holder:
- Xie et al
- Copyright date:
- 2015
- Notes:
- DLMIA 2015 - MICCAI workshop.
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