Collaborative colorectal cancer classification on highly class imbalanced data setting via federated neural style transfer based data augmentation

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dc.authorid0000-0002-0867-5518en_US
dc.contributor.authorNergiz, Mehmet
dc.date.accessioned2023-08-10T07:19:56Z
dc.date.available2023-08-10T07:19:56Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe deep learning algorithms achieved promising results in the computational pathology in recent decade but the high data demand of the deep learning algorithms get stuck in the multi-institutional data collaborations. The federated learning is a novel concept, which proposes to train the models of the different sites collaboratively via an orchestrating server without leaking private data. However, the imbalanced data distributions are challenging for federated learning and result in performance decrease and destabilization. In this study, the federated version of the neural style transfer algorithm, which was offered by Gatys et al. is proposed as a data augmentation method on the highly class imbalanced configuration of Chaoyang colorectal cancer imaging dataset. The proposed method works by firstly selecting characteristic style images and then generating the gram style matrices on the local sites and then transferring them to the other imbalanced sites by not leaking any private data. The proposed method contributed the ACC, F1 Score and AUC results of pure FL by 22.07%, 42.51% and 9.65% using only 20 images for content and 5 images for style. Additionally, the experiments having different content and style numbers achieved the satisfactory and consisting results.en_US
dc.identifier.citationNergiz, M. (2022). Collaborative colorectal cancer classification on highly class imbalanced data setting via federated neural style transfer based data augmentation. Traitement du Signal, 39(6), 2077-2086.en_US
dc.identifier.doi10.18280/ts.390620
dc.identifier.endpage2086en_US
dc.identifier.issn0765-0019
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85150178936
dc.identifier.scopusqualityQ3
dc.identifier.startpage2077en_US
dc.identifier.urihttps://www.iieta.org/journals/ts/paper/10.18280/ts.390620
dc.identifier.urihttps://hdl.handle.net/11468/12461
dc.identifier.volume39en_US
dc.identifier.wosWOS:000944709500020
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorNergiz, Mehmet
dc.language.isoenen_US
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofTraitement du Signal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFederated learningen_US
dc.subjectNeural style transferen_US
dc.subjectConvolutional neural networken_US
dc.subjectColorectal canceren_US
dc.subjectComputational pathologyen_US
dc.titleCollaborative colorectal cancer classification on highly class imbalanced data setting via federated neural style transfer based data augmentationen_US
dc.titleCollaborative colorectal cancer classification on highly class imbalanced data setting via federated neural style transfer based data augmentation
dc.typeArticleen_US

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