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Improving the Pap test with artificial intelligence
dc.contributor.authorPantanowitz, Liron
dc.date.accessioned2022-07-05T21:00:15Z
dc.date.available2023-07-05 17:00:14en
dc.date.available2022-07-05T21:00:15Z
dc.date.issued2022-06
dc.identifier.citationPantanowitz, Liron (2022). "Improving the Pap test with artificial intelligence." Cancer Cytopathology (6): 402-404.
dc.identifier.issn1934-662X
dc.identifier.issn1934-6638
dc.identifier.urihttps://hdl.handle.net/2027.42/172945
dc.publisherWiley Periodicals, Inc.
dc.subject.otherpap test
dc.subject.othercervical cancer
dc.subject.othercomputational pathology
dc.subject.othercytology
dc.subject.otherdeep learning
dc.subject.otherartificial intelligence
dc.titleImproving the Pap test with artificial intelligence
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelOncology and Hematology
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172945/1/cncy22561.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/172945/2/cncy22561_am.pdf
dc.identifier.doi10.1002/cncy.22561
dc.identifier.sourceCancer Cytopathology
dc.identifier.citedreferenceLew M, Wilbur DC, Pantanowitz L. Computational cytology: lessons learned from Pap test computer-assisted screening. Acta Cytol. 2021; 49: 921 - 927.
dc.identifier.citedreferenceMalinowski DP, Broache M, Vaughan L, et al. Cotesting in cervical cancer screening. Am J Clin Pathol. 2021; 155: 150 - 154.
dc.identifier.citedreferenceAbels E, Pantanowitz L, Aeffner F, et al. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol Inform. 2019; 249: 286 - 294.
dc.identifier.citedreferenceTao X, Chu X, Guo B, et al. Scrutinizing high-risk patients from ASCUS cytology via a deep learning model. Cancer Cytopathol. 2022; 130: 407 - 414.
dc.identifier.citedreferenceSong Y, Zhang L, Chen S, et al. A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei. Conf Proc IEEE Eng Med Biol Soc. 2014; 2014: 2903 - 2906.
dc.identifier.citedreferenceChankong T, Theera-Umpon N, Auephanwiriyakul S. Automatic cervical cell segmentation and classification in Pap smears. Comput Methods Programs Biomed. 2014; 113: 539 - 556.
dc.identifier.citedreferenceZhao L, Li K, Wang M, et al. Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF. Comput Biol Med. 2016; 71: 46 - 56.
dc.identifier.citedreferenceZhang L, Le L, Nogues I, Summers RM, Liu S, Yao J. DeepPap: Deep Convolutional Networks for Cervical Cell Classification. IEEE J Biomed Health Inform. 2017; 21: 1633 - 1643.
dc.identifier.citedreferenceBora K, Chowdhury M, Mahanta LB, Kundu MK, Das AK. Automated classification of Pap smear images to detect cervical dysplasia. Comput Methods Programs Biomed. 2017; 138: 31 - 47.
dc.identifier.citedreferenceWilliam W, Ware A, Basaza-Ejiri AH, Obungoloch J. A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images. Comput Methods Programs Biomed. 2018; 164: 15 - 22.
dc.identifier.citedreferenceLandau M, Pantanowitz L. Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape. J Am Soc Cytopathol. 2019; 8: 230 - 241.
dc.identifier.citedreferencePantanowitz L, Bui MM. Modern techniques in cytopathology. Monogr Clin Cytol. 2020; 25: 67 - 74.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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