{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:58:28Z","timestamp":1765486708359,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,29]]},"DOI":"10.1145\/3500931.3501004","type":"proceedings-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T05:13:49Z","timestamp":1640236429000},"page":"431-437","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Mammography Data Augmentation Using ACGAN"],"prefix":"10.1145","author":[{"given":"Yijiang","family":"Fan","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai, China"}]},{"given":"Jiajia","family":"Jiao","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Momenimovahed Z. and H. Salehiniya. \"Epidemiological characteristics of and risk factors for breast cancer in the world.\" Breast Cancer: Targets and Therapy 11(2019).  Momenimovahed Z. and H. Salehiniya. \"Epidemiological characteristics of and risk factors for breast cancer in the world.\" Breast Cancer: Targets and Therapy 11(2019).","DOI":"10.2147\/BCTT.S176070"},{"key":"e_1_3_2_1_2_1","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition.\" Computer Science","author":"Simonyan K.","year":"2014","unstructured":"Simonyan , K. , and A. Zisserman . \" Very Deep Convolutional Networks for Large-Scale Image Recognition.\" Computer Science ( 2014 ). Simonyan, K., and A. Zisserman. \"Very Deep Convolutional Networks for Large-Scale Image Recognition.\" Computer Science (2014)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-bioeng-071516-044442"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2010.05.019"},{"key":"e_1_3_2_1_5_1","first-page":"4","article-title":"Breast image feature learning with adaptive deconvolutional networks","volume":"8315","author":"Jamieson A. R.","year":"2012","unstructured":"Jamieson , A. R. , et al. \" Breast image feature learning with adaptive deconvolutional networks .\" Proceedings of SPIE - The International Society for Optical Engineering 8315 ( 2012 ): 4 . Jamieson, A. R., et al. \"Breast image feature learning with adaptive deconvolutional networks.\" Proceedings of SPIE - The International Society for Optical Engineering 8315(2012):4.","journal-title":"Proceedings of SPIE - The International Society for Optical Engineering"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"e_1_3_2_1_7_1","first-page":"2672","article-title":"Generative Adversarial Networks","volume":"3","author":"Goodfellow I. J.","year":"2014","unstructured":"Goodfellow , I. J. , et al. \" Generative Adversarial Networks .\" Advances in Neural Information Processing Systems 3 ( 2014 ): 2672 -- 2680 . Goodfellow, I. J., et al. \"Generative Adversarial Networks.\" Advances in Neural Information Processing Systems 3(2014):2672--2680.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_8_1","volume-title":"PGGAN-based Data Augmentation for Tumor Detection","author":"Han C.","year":"2019","unstructured":"Han , C. , et al. \"Infinite Brain MR Images : PGGAN-based Data Augmentation for Tumor Detection .\" ( 2019 ). Han, C., et al. \"Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection.\" (2019)."},{"key":"e_1_3_2_1_9_1","first-page":"1","article-title":"Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks","volume":"99","author":"Munawar F.","year":"2020","unstructured":"Munawar , F. , et al. \" Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks .\" IEEE Access PP . 99 ( 2020 ): 1 -- 1 . Munawar, F., et al. \"Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks.\" IEEE Access PP.99(2020):1--1.","journal-title":"IEEE Access PP"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2083676"},{"key":"e_1_3_2_1_11_1","first-page":"53","article-title":"Generative Adversarial Networks: An Overview","volume":"1","author":"Creswell A.","year":"2017","unstructured":"Creswell , A. , et al. \" Generative Adversarial Networks: An Overview .\" IEEE Signal Processing Magazine 35 . 1 ( 2017 ): 53 -- 65 . Creswell, A., et al. \"Generative Adversarial Networks: An Overview.\" IEEE Signal Processing Magazine 35.1(2017):53--65.","journal-title":"IEEE Signal Processing Magazine 35"},{"key":"e_1_3_2_1_12_1","volume-title":"Conditional Image Synthesis With Auxiliary Classifier GANs","author":"Odena A.","year":"2016","unstructured":"Odena , A. , C. Olah , and J. Shlens . \" Conditional Image Synthesis With Auxiliary Classifier GANs .\" ( 2016 ). Odena, A., C. Olah, and J. Shlens. \"Conditional Image Synthesis With Auxiliary Classifier GANs.\" (2016)."},{"key":"e_1_3_2_1_13_1","volume-title":"Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection","author":"Waheed A.","year":"2021","unstructured":"Waheed , A. , et al. \"Covid GAN : Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection .\" ( 2021 ). Waheed, A., et al. \"CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection.\" (2021)."},{"key":"e_1_3_2_1_14_1","volume-title":"\" arXiv","author":"Vaswani","year":"2017","unstructured":"Vaswani , Ashish, et al. \"Attention Is All You Need. \" arXiv ( 2017 ). Vaswani, Ashish, et al. \"Attention Is All You Need.\" arXiv (2017)."},{"key":"e_1_3_2_1_15_1","unstructured":"Zhang H. et al. \"Self-Attention Generative Adversarial Networks.\" (2018).  Zhang H. et al. \"Self-Attention Generative Adversarial Networks.\" (2018)."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Learning Representations","author":"Miyato T.","year":"2018","unstructured":"Miyato , T. , et al. \"Spectral Normalization for Generative Adversarial Networks.\" International Conference on Learning Representations 2018 . Miyato, T., et al. \"Spectral Normalization for Generative Adversarial Networks.\" International Conference on Learning Representations 2018."},{"key":"e_1_3_2_1_17_1","volume-title":"When Does Label Smoothing Help?","author":"M\u00fcller S.","year":"2019","unstructured":"M\u00fcller , Rafael, S. Kornblith, and G. Hinton . \" When Does Label Smoothing Help? \" ( 2019 ). M\u00fcller, Rafael, S. Kornblith, and G. Hinton. \"When Does Label Smoothing Help?\" (2019)."},{"key":"e_1_3_2_1_18_1","volume-title":"A note on the evaluation of generative models.\" Computer ence","author":"Theis L.","year":"2016","unstructured":"Theis , L. , A. V. D. Oord , and M. Bethge . \" A note on the evaluation of generative models.\" Computer ence ( 2016 ). Theis, L., A. V. D. Oord, and M. Bethge. \"A note on the evaluation of generative models.\" Computer ence (2016)."},{"key":"e_1_3_2_1_19_1","unstructured":"Zhang F. et al. \"Three-branch and Mutil-scale learning for Fine-grained Image Recognition (TBMSL-Net).\" (2020).  Zhang F. et al. \"Three-branch and Mutil-scale learning for Fine-grained Image Recognition (TBMSL-Net).\" (2020)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Loey M. F. Smarandache and N. Khalifa. \"A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images.\" Neural Computing and Applications (2020): 1--13.  Loey M. F. Smarandache and N. Khalifa. \"A Deep Transfer Learning Model with Classical Data Augmentation and CGAN to Detect COVID-19 from Chest CT Radiography Digital Images.\" Neural Computing and Applications (2020):1--13.","DOI":"10.20944\/preprints202004.0252.v1"}],"event":{"name":"ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences","acronym":"ISAIMS 2021","location":"Beijing China"},"container-title":["Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3500931.3501004","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3500931.3501004","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:50:52Z","timestamp":1750182652000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3500931.3501004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":20,"alternative-id":["10.1145\/3500931.3501004","10.1145\/3500931"],"URL":"https:\/\/doi.org\/10.1145\/3500931.3501004","relation":{},"subject":[],"published":{"date-parts":[[2021,10,29]]},"assertion":[{"value":"2021-12-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}