{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T11:34:51Z","timestamp":1782732891330,"version":"3.54.5"},"reference-count":39,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019MF012"],"award-info":[{"award-number":["ZR2019MF012"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["61873281"],"award-info":[{"award-number":["61873281"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["61873280"],"award-info":[{"award-number":["61873280"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["61972416"],"award-info":[{"award-number":["61972416"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2019MF012"],"award-info":[{"award-number":["ZR2019MF012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873281"],"award-info":[{"award-number":["61873281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873280"],"award-info":[{"award-number":["61873280"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972416"],"award-info":[{"award-number":["61972416"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,7,1]]},"abstract":"<jats:p>Histological analysis to tissue samples is elemental for diagnosing the risk and severity of ovarian cancer. The commonly used Hematoxylin and Eosin (H&amp;E) staining method involves complex steps and strict requirements, which would seriously impact the research of histological analysis of the ovarian cancer. Virtual histological staining by the Generative Adversarial Network (GAN) provides a feasible way for these problems, yet it is still a challenge of using deep learning technology since the amounts of data available are quite limited for training. Based on the idea of GAN, we propose a weakly supervised learning method to generate autofluorescence images of unstained ovarian tissue sections corresponding to H&amp;E staining sections of ovarian tissue. Using the above method, we constructed the supervision conditions for the virtual staining process, which makes the image quality synthesized in the subsequent virtual staining stage more perfect. Through the doctors\u2019 evaluation of our results, the accuracy of ovarian cancer unstained fluorescence image generated by our method reached 93%. At the same time, we evaluated the image quality of the generated images, where the FID reached 175.969, the IS score reached 1.311, and the MS reached 0.717. Based on the image-to-image translation method, we use the data set constructed in the previous step to implement a virtual staining method that is accurate to tissue cells. The accuracy of staining through the doctor\u2019s assessment reached 97%. At the same time, the accuracy of visual evaluation based on deep learning reached 95%.<\/jats:p>","DOI":"10.1155\/2021\/4244157","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T20:35:47Z","timestamp":1625258147000},"page":"1-12","source":"Crossref","is-referenced-by-count":30,"title":["A Computationally Virtual Histological Staining Method to Ovarian Cancer Tissue by Deep Generative Adversarial Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5696-3090","authenticated-orcid":true,"given":"Xiangyu","family":"Meng","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580 Shandong, China"},{"name":"College of Computer and Information Science, Inner Mongolia Agricultural University, Huhhot, 010018 Inner Mongolia, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7643-4898","authenticated-orcid":true,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gynecology 2, Renmin Hospital of Wuhan University, Wuhan, 430060 Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2741-433X","authenticated-orcid":true,"given":"Xun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580 Shandong, China"},{"name":"China High Performance Computer Research Center, Institute of Computer Technology, Chinese Academy of Science, Beijing, 100190 Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/TNB.2019.2943923"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.08.055"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2675-z"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1038\/srep27624"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.3390\/molecules23061307"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2962862"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.3390\/biom11050643"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2021.638330"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1515\/med-2020-0028"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.3390\/sym12121997"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2016.61"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105506"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.prp.2018.03.021"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-019-0362-y"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.244"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"17","article-title":"Generative adversarial networks","author":"I. J. Goodfellow","year":"2014"},{"key":"18","article-title":"Wasserstein GAN","author":"M. Arjovsky","year":"2017"},{"key":"19","article-title":"Improved training of wasserstein gans","author":"I. Gulrajani","year":"2017"},{"key":"20","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"A. Radford","year":"2015"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.632"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00917"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.19"},{"key":"25","article-title":"Conditional generative adversarial nets","author":"M. Mirza","year":"2014"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00916"},{"key":"27","first-page":"1857","article-title":"Learning to discover cross-domain relations with generative adversarial networks","author":"T. Kim"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.578"},{"key":"29","article-title":"Towards principled methods for training generative adversarial networks","author":"M. Arjovsky","year":"2017"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.168"},{"key":"31","article-title":"Video-to-video synthesis","author":"T.-C. Wang","year":"2018"},{"key":"32","article-title":"Progressive growing of gans for improved quality, stability, and variation","author":"T. Karras","year":"2017"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2019.00453"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr42600.2020.00813"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.195"},{"key":"37","doi-asserted-by":"publisher","DOI":"10.1109\/icassp40776.2020.9053405"},{"key":"38","first-page":"3481","article-title":"Which training methods for GANs do actually converge?","author":"L. Mescheder"},{"key":"39","article-title":"Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients","author":"A. Ross"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/4244157.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/4244157.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/4244157.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T20:35:51Z","timestamp":1625258151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2021\/4244157\/"}},"subtitle":[],"editor":[{"given":"Pan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,7,1]]},"references-count":39,"alternative-id":["4244157","4244157"],"URL":"https:\/\/doi.org\/10.1155\/2021\/4244157","relation":{},"ISSN":["1748-6718","1748-670X"],"issn-type":[{"value":"1748-6718","type":"electronic"},{"value":"1748-670X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,7,1]]}}}