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Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16770
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dc.contributor.authorSikhakolli, Sravan Kumar-
dc.contributor.authorAala, Suresh-
dc.contributor.authorChinnadurai, Sunil-
dc.contributor.authorMuniraj, Inbarasan-
dc.contributor.authorDeshpande, Anuj-
dc.date.accessioned2024-12-12T09:29:59Z-
dc.date.available2024-12-12T09:29:59Z-
dc.date.issued2024-
dc.identifier.isbn9781957171371-
dc.identifier.urihttps://opg.optica.org/abstract.cfm?URI=3D-2024-DW3H.3-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16770-
dc.description.abstractThis article introduces a novel semi-supervised learning method for Cholangiocarcinoma detection using inherent statistical parameters of the image on the multidimensional Choledochal dataset. Results closely match the pathologist’s annotations, validated by image similarity indices. © 2024 The Author(s).en_US
dc.language.isoenen_US
dc.publisherOptica Imaging Congress 2024 (3D, AOMS, COSI, ISA, pcAOP)en_US
dc.publisherOptical Society of Americaen_US
dc.subjectAdversarial Machine Learningen_US
dc.subjectContrastive Learningen_US
dc.subjectFederated Learningen_US
dc.subjectSemi-Supervised Learningen_US
dc.subjectCholangiocarcinomaen_US
dc.titleCholangiocarcinoma Classification Using Semi-Supervised Learning Approachen_US
dc.typeArticleen_US
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