Unsupervised morphological segmentation of tissue compartments in histopathological images
- Submitting institution
-
Birmingham City University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11Z_OP_D0052
- Type
- D - Journal article
- DOI
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10.1371/journal.pone.0188717
- Title of journal
- PLOS ONE
- Article number
- e0188717
- First page
- -
- Volume
- 12
- Issue
- 11
- ISSN
- 1932-6203
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188717
- Supplementary information
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-
- 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
-
-
- Research group(s)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper proposes an unsupervised segmentation framework for identifying epithelial cancerous tissues in tissue micro-arrays. Experimental results proved the superiority of the algorithm over eight popular tissue segmentation strategies. The wide adoption of this method will lead to enhancing medical image analysis, improving segmentation quality and avoiding the expensive data annotation in images. The output of this research gained recognition in the field of digital pathology, and it helped to inspire further developments in the area of intelligent cancer diagnosis. It was cited by 16 papers, I provide below number of DOIs of the citing papers: https://doi.org/10.1007/978-3-030-32239-7_67, https://doi.org/10.1016/j.compmedimag.2019.101686. , https://doi.org/10.24018/ejers.2019.4.2.1142.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -