{"id":14847,"date":"2024-01-10T20:40:15","date_gmt":"2024-01-10T19:40:15","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=14847"},"modified":"2024-01-10T20:40:15","modified_gmt":"2024-01-10T19:40:15","slug":"cell-image-augmentation-for-classification-task-using-gans-on-pap-smear-dataset","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/cell-image-augmentation-for-classification-task-using-gans-on-pap-smear-dataset\/","title":{"rendered":"Cell image augmentation for classification task using GANs on Pap Smear Dataset"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Jakub Zak, Michal K Grzeszczyk, Antonina Pater, Lukasz Roszkowiak, Krzysztof Siemion, Anna Korzynska<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">One of the solutions to the problem of insufficiently large training datasets in\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/image-processing\">image processing<\/a>\u00a0is data augmentation. This process artificially extends the size of training datasets to avoid overfitting. Generative Adversarial Networks yield that become increasingly difficult to differentiate from real images, until the differentiation is no longer possible. Thus, artificial images closely resembling original ones can be generated. Inclusion of artificial images contributes to improving the training process. Medical domain is one of the areas where data acquisition is burdened by many procedures, laws, and prohibitions. As a result the potential size of collected datasets is reduced. This article presents the results of training Convolutional\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/chemical-engineering\/neural-network\">Neural Networks<\/a>\u00a0on an artificially extended image datasets. The resulting\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/classification-accuracy\">classification accuracy<\/a>\u00a0on a cell\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/classification-task\">classification tas<\/a>k of models trained with images generated using the proposed method were increased by up to 12.9% in comparison to that of the model trained only with original dataset from the HErlev Pap smear dataset.<\/p>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0208521622000675?fr=RR-2&#038;ref=pdf_download&#038;rr=84376252bc5bb351\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>In: Biocybernetics and Biomedical Engineering Journal (IF: 5.687), 2022.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[17],"class_list":["post-14847","research","type-research","status-publish","hentry","research_type-publications","research_team-health-informatics-group-higs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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This process artificially extends the size of training datasets to avoid overfitting. Generative Adversarial Networks yield that become increasingly difficult to differentiate from real images, until the differentiation is no longer possible. Thus, artificial images closely resembling original ones can be generated. Inclusion of artificial images contributes to improving the training process. Medical domain is one of the areas where data acquisition is burdened by many procedures, laws, and prohibitions. As a result the potential size of collected datasets is reduced. This article presents the results of training Convolutional\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/chemical-engineering\/neural-network\">Neural Networks<\/a>\u00a0on an artificially extended image datasets. The resulting\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/classification-accuracy\">classification accuracy<\/a>\u00a0on a cell\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/classification-task\">classification tas<\/a>k of models trained with images generated using the proposed method were increased by up to 12.9% in comparison to that of the model trained only with original dataset from the HErlev Pap smear dataset.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">One of the solutions to the problem of insufficiently large training datasets in\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/image-processing\">image processing<\/a>\u00a0is data augmentation. This process artificially extends the size of training datasets to avoid overfitting. Generative Adversarial Networks yield that become increasingly difficult to differentiate from real images, until the differentiation is no longer possible. Thus, artificial images closely resembling original ones can be generated. Inclusion of artificial images contributes to improving the training process. Medical domain is one of the areas where data acquisition is burdened by many procedures, laws, and prohibitions. As a result the potential size of collected datasets is reduced. This article presents the results of training Convolutional\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/chemical-engineering\/neural-network\">Neural Networks<\/a>\u00a0on an artificially extended image datasets. The resulting\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/classification-accuracy\">classification accuracy<\/a>\u00a0on a cell\u00a0<a href=\"https:\/\/www.sciencedirect.com\/topics\/engineering\/classification-task\">classification tas<\/a>k of models trained with images generated using the proposed method were increased by up to 12.9% in comparison to that of the model trained only with original dataset from the HErlev Pap smear dataset.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-jt2dsT","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0208521622000675?fr=RR-2&ref=pdf_download&rr=84376252bc5bb351","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14847","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":2,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14847\/revisions"}],"predecessor-version":[{"id":14849,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/14847\/revisions\/14849"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=14847"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=14847"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=14847"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}