Hover-Net : simultaneous segmentation and classification of nuclei in multi-tissue histology images
- Submitting institution
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The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12725
- Type
- D - Journal article
- DOI
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10.1016/j.media.2019.101563
- Title of journal
- Medical Image Analysis
- Article number
- 101563
- First page
- -
- Volume
- 58
- Issue
- -
- ISSN
- 1361-8415
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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6
- Research group(s)
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A - Applied Computing
- Citation count
- 28
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This highly cited article, published in a top journal in medical imaging, presented a novel deep learning method that proposes to utilise multiple heads for simultaneous segmentation and classification of nuclei in cancer histology images. The method is considered state-of-the-art for segmentation and classification of various types of nuclei in histology images, as evidenced by its winning the recently held MONuSaC challenge contest [https://monusac-2020.grand-challenge.org/Results/]. In the space of few months, it has led to further developments by several leading research groups (Tao, Syndey; Mikut, Karlsruhe; Paragios, EC Paris) and a collaboration with Nvidia (contact: Jonny Hancox, jhancox@nvidia.com) on hardware optimisation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -