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This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output.<\/jats:p>","DOI":"10.3390\/s22103960","type":"journal-article","created":{"date-parts":[[2022,5,25]],"date-time":"2022-05-25T00:14:14Z","timestamp":1653437654000},"page":"3960","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation"],"prefix":"10.3390","volume":"22","author":[{"given":"Juwon","family":"Kweon","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Korea"}]},{"given":"Jisang","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Korea"}]},{"given":"Seungjong","family":"Kim","sequence":"additional","affiliation":[{"name":"Molpaxbio, Daejeon 34047, Korea"}]},{"given":"Jaesik","family":"Won","sequence":"additional","affiliation":[{"name":"Molpaxbio, Daejeon 34047, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6595-6415","authenticated-orcid":false,"given":"Soonchul","family":"Kwon","sequence":"additional","affiliation":[{"name":"Graduate School of Smart Convergence, Kwangwoon University, Seoul 01897, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. 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