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The following was done to evaluate each method\u2019s performance: the similarities between the generalized image and the original were measured via the structural similarity index (SSIM) and histogram, and the original domain data set was passed to a classifier that trained only the original domain images for accuracy comparisons. The results show that the performance evaluation of the generalized images is better than that of the originals, confirming that our proposed method is a simple but powerful solution to the performance degradation of a classification network.<\/jats:p>","DOI":"10.1186\/s13673-020-00220-2","type":"journal-article","created":{"date-parts":[[2020,4,25]],"date-time":"2020-04-25T14:02:48Z","timestamp":1587823368000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Generalization of intensity distribution of medical images using GANs"],"prefix":"10.1186","volume":"10","author":[{"given":"Dong-Ho","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byeong-Seok","family":"Shin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,25]]},"reference":[{"key":"220_CR1","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G et al (2017) A survey on deep learning in medical image analysis. 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