Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/14624
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dc.contributor.authorMathew T.
dc.contributor.authorKini J.R.
dc.contributor.authorRajan J.
dc.date.accessioned2021-05-05T10:11:44Z-
dc.date.available2021-05-05T10:11:44Z-
dc.date.issued2021
dc.identifier.citationBiocybernetics and Biomedical Engineering , Vol. 41 , 1 , p. 64 - 82en_US
dc.identifier.urihttps://doi.org/10.1016/j.bbe.2020.11.005
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/14624-
dc.description.abstractMitosis detection is an important step in pathology procedures in the context of cancer diagnosis and prognosis. Prevalent process for this task is by manually observing Hematoxylin and Eosin (H & E) stained histopathology sections on glass slides through a microscope by trained pathologists. This conventional approach is tedious, error-prone, and has shown high inter-observer variability. With the advancement of computational technologies, automating mitosis detection by the use of image processing algorithms has attracted significant research interest. In the past decade, several methods appeared in the literature, addressing this problem and they have shown encouraging incremental progress towards a clinically usable solution. Mitosis count is an important parameter in grading of breast cancer and glioma, unlike other cancer types. Driven by the availability of multiple public datasets and open contests, most of the methods in literature address mitosis detection in breast cancer images. This paper is a comprehensive review of the methods published in the area of automated mitotic cell detection in H & E stained histopathology images of breast cancer in the last 10 years. We also discuss the current trends and future prospects of this clinically relevant task, augmenting humanity's fight against cancer. © 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciencesen_US
dc.titleComputational methods for automated mitosis detection in histopathology images: A reviewen_US
dc.typeReviewen_US
Appears in Collections:5. Miscellaneous Publications

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