A hybrid deep learning approach for gland segmentation in prostate histopathological images
Article
Article Title | A hybrid deep learning approach for gland segmentation in prostate histopathological images |
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ERA Journal ID | 5031 |
Article Category | Article |
Authors | Salvi, Massimo, Bosco, Martino, Molinaro, Luca, Gambella, Alessandro, Papotti, Mauro, Acharya, U. Rajendra and Molinari, Filippo |
Journal Title | Artificial Intelligence in Medicine |
Journal Citation | 115 |
Article Number | 102076 |
Number of Pages | 12 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0933-3657 |
1873-2860 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.artmed.2021.102076 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0933365721000695 |
Abstract | Background Method Results Conclusions |
Keywords | Computer-aided image analysis; Glands segmentation ; Prostate cancer ; Deep learning ; Digital pathology |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | PoliToBIOMed Lab, Italy |
San Lazzaro Hospital, Italy | |
A.O.U. Città della Salute e della Scienza Hospital, Italy | |
University of Turin, Italy | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/z1w04/a-hybrid-deep-learning-approach-for-gland-segmentation-in-prostate-histopathological-images
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