{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T22:29:05Z","timestamp":1783031345417,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,11,3]],"date-time":"2019-11-03T00:00:00Z","timestamp":1572739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"AMED","award":["P18lk1010028"],"award-info":[{"award-number":["P18lk1010028"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,11,3]]},"DOI":"10.1145\/3357384.3357890","type":"proceedings-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T14:11:35Z","timestamp":1572876695000},"page":"119-127","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":78,"title":["Learning More with Less"],"prefix":"10.1145","author":[{"given":"Changhee","family":"Han","sequence":"first","affiliation":[{"name":"National Institute of Informatics, National Center for Global Health and Medicine, & University of Cambridge, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kohei","family":"Murao","sequence":"additional","affiliation":[{"name":"National Institute of Informatics & National Center for Global Health and Medicine, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoyuki","family":"Noguchi","sequence":"additional","affiliation":[{"name":"National Center for Global Health and Medicine, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuke","family":"Kawata","sequence":"additional","affiliation":[{"name":"National Center for Global Health and Medicine, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fumiya","family":"Uchiyama","sequence":"additional","affiliation":[{"name":"National Center for Global Health and Medicine, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Rundo","sequence":"additional","affiliation":[{"name":"University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hideki","family":"Nakayama","sequence":"additional","affiliation":[{"name":"The University of Tokyo, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shin'ichi","family":"Satoh","sequence":"additional","affiliation":[{"name":"National Institute of Informatics & The University of Tokyo, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,11,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340","author":"Antoniou Antreas","year":"2017","unstructured":"Antreas Antoniou , Amos Storkey , and Harrison Edwards . 2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 ( 2017 ). Antreas Antoniou, Amos Storkey, and Harrison Edwards. 2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 (2017)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/neuonc\/now127"},{"key":"e_1_3_2_1_3_1","volume-title":"Red blood cell image generation for data augmentation using conditional generative adversarial networks. arXiv preprint arXiv:1901.06219","author":"Bailo Oleksandr","year":"2019","unstructured":"Oleksandr Bailo , DongShik Ham , and Young Min Shin . 2019. Red blood cell image generation for data augmentation using conditional generative adversarial networks. arXiv preprint arXiv:1901.06219 ( 2019 ). Oleksandr Bailo, DongShik Ham, and Young Min Shin. 2019. Red blood cell image generation for data augmentation using conditional generative adversarial networks. arXiv preprint arXiv:1901.06219 (2019)."},{"key":"e_1_3_2_1_4_1","first-page":"110","article-title":"Ensemble learning","volume":"2","author":"Dietterich Thomas G","year":"2002","unstructured":"Thomas G Dietterich . 2002 . Ensemble learning . The Handbook of Brain Theory and Neural Networks 2 (2002), 110 -- 125 . Thomas G Dietterich. 2002. Ensemble learning. The Handbook of Brain Theory and Neural Networks 2 (2002), 110--125.","journal-title":"The Handbook of Brain Theory and Neural Networks"},{"key":"e_1_3_2_1_5_1","volume-title":"Proc. Machine Learning for Health (ML4H) Workshop arXiv:1812","author":"Finlayson Samuel G","year":"2018","unstructured":"Samuel G Finlayson , Hyunkwang Lee , Isaac S Kohane , and Luke Oakden-Rayner . 2018 . Towards generative adversarial networks as a new paradigm for radiology education . In Proc. Machine Learning for Health (ML4H) Workshop arXiv:1812 .01547. Samuel G Finlayson, Hyunkwang Lee, Isaac S Kohane, and Luke Oakden-Rayner. 2018. Towards generative adversarial networks as a new paradigm for radiology education. In Proc. Machine Learning for Health (ML4H) Workshop arXiv:1812.01547."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"e_1_3_2_1_7_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley etal 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS). 2672--2680.  Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley et al. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS). 2672--2680."},{"key":"e_1_3_2_1_8_1","volume-title":"Courville","author":"Gulrajani Ishaan","year":"2017","unstructured":"Ishaan Gulrajani , Faruk Ahmed , Mart\u00edn Arjovsky , Vincent Dumoulin , and Aaron C . Courville . 2017 . Improved Training of Wasserstein GANs . arXiv preprint arXiv:1704.00028 (2017). Ishaan Gulrajani, Faruk Ahmed, Mart\u00edn Arjovsky, Vincent Dumoulin, and Aaron C. Courville. 2017. Improved Training of Wasserstein GANs. arXiv preprint arXiv:1704.00028 (2017)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363678"},{"key":"#cr-split#-e_1_3_2_1_11_1.1","unstructured":"Changhee Han Leonardo Rundo Ryosuke Araki Yujiro Furukawa Giancarlo Mauri etal 2019. Infinite brain MR images: PGGAN-based data augmentation for tumor detection. In Neural Approaches to Dynamics of Signal Exchanges. Springer. https:\/\/doi.org\/10.1007\/978--981--13--8950--4_27 (In press). 10.1007\/978--981--13--8950--4_27"},{"key":"#cr-split#-e_1_3_2_1_11_1.2","doi-asserted-by":"crossref","unstructured":"Changhee Han Leonardo Rundo Ryosuke Araki Yujiro Furukawa Giancarlo Mauri et al. 2019. Infinite brain MR images: PGGAN-based data augmentation for tumor detection. In Neural Approaches to Dynamics of Signal Exchanges. Springer. https:\/\/doi.org\/10.1007\/978--981--13--8950--4_27 (In press).","DOI":"10.1007\/978-981-13-8950-4_27"},{"key":"e_1_3_2_1_12_1","volume-title":"Proc. European Conference on Computer Vision (ECCV). 718--731","author":"Huang Sheng-Wei","year":"2018","unstructured":"Sheng-Wei Huang , Che-Tsung Lin , Shu-Ping Chen , Yen-Yi Wu , Po-Hao Hsu , and Shang-Hong Lai . 2018 . AugGAN: cross domain adaptation with GAN-based data augmentation . In Proc. European Conference on Computer Vision (ECCV). 718--731 . Sheng-Wei Huang, Che-Tsung Lin, Shu-Ping Chen, Yen-Yi Wu, Po-Hao Hsu, and Shang-Hong Lai. 2018. AugGAN: cross domain adaptation with GAN-based data augmentation. In Proc. European Conference on Computer Vision (ECCV). 718--731."},{"key":"e_1_3_2_1_13_1","volume-title":"Proc. International Conference on Learning Representations (ICLR) arXiv:1502","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch normalization: accelerating deep network training by reducing internal covariate shift . In Proc. International Conference on Learning Representations (ICLR) arXiv:1502 .03167. Sergey Ioffe and Christian Szegedy. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proc. International Conference on Learning Representations (ICLR) arXiv:1502.03167."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_81"},{"key":"e_1_3_2_1_16_1","volume-title":"Proc. International Conference on Learning Representations (ICLR) arXiv:1710","author":"Karras Tero","year":"2018","unstructured":"Tero Karras , Timo Aila , Samuli Laine , and Jaakko Lehtinen . 2018 . Progressive growing of GANs for improved quality, stability, and variation . In Proc. International Conference on Learning Representations (ICLR) arXiv:1710 .10196v3. Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2018. Progressive growing of GANs for improved quality, stability, and variation. In Proc. International Conference on Learning Representations (ICLR) arXiv:1710.10196v3."},{"key":"e_1_3_2_1_17_1","volume-title":"A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948","author":"Karras Tero","year":"2018","unstructured":"Tero Karras , Samuli Laine , and Timo Aila . 2018. A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948 ( 2018 ). Tero Karras, Samuli Laine, and Timo Aila. 2018. A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948 (2018)."},{"key":"e_1_3_2_1_18_1","volume-title":"Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 ( 2014 ). Diederik P Kingma and Jimmy Ba. 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_19_1","volume-title":"Proc. International Conference on Learning Representations (ICLR) arXiv:1312","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling . 2013 . Auto-encoding variational bayes . In Proc. International Conference on Learning Representations (ICLR) arXiv:1312 .6114. Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. In Proc. International Conference on Learning Representations (ICLR) arXiv:1312.6114."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"e_1_3_2_1_21_1","volume-title":"BAGAN: data augmentation with balancing GAN. arXiv preprint arXiv:1803.09655","author":"Mariani Giovanni","year":"2018","unstructured":"Giovanni Mariani , Florian Scheidegger , Roxana Istrate , Costas Bekas , and Cristiano Malossi . 2018. BAGAN: data augmentation with balancing GAN. arXiv preprint arXiv:1803.09655 ( 2018 ). Giovanni Mariani, Florian Scheidegger, Roxana Istrate, Costas Bekas, and Cristiano Malossi. 2018. BAGAN: data augmentation with balancing GAN. arXiv preprint arXiv:1803.09655 (2018)."},{"key":"e_1_3_2_1_22_1","volume-title":"Proc. International Conference on Machine Learning (ICML). 2391--2400","author":"Mescheder Lars","year":"2017","unstructured":"Lars Mescheder , Sebastian Nowozin , and Andreas Geiger . 2017 . Adversarial variational bayes: unifying variational autoencoders and generative adversarial networks . In Proc. International Conference on Machine Learning (ICML). 2391--2400 . Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017. Adversarial variational bayes: unifying variational autoencoders and generative adversarial networks. In Proc. International Conference on Machine Learning (ICML). 2391--2400."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"e_1_3_2_1_24_1","volume-title":"Pedestrian-Synthesis-GAN: generating pedestrian data in real scene and beyond. arXiv preprint arXiv","author":"Ouyang Xi","year":"1804","unstructured":"Xi Ouyang , Yu Cheng , Yifan Jiang , Chun-Liang Li ,and Pan Zhou . 2018. Pedestrian-Synthesis-GAN: generating pedestrian data in real scene and beyond. arXiv preprint arXiv : 1804 .02047 (2018). Xi Ouyang, Yu Cheng, Yifan Jiang, Chun-Liang Li,and Pan Zhou. 2018. Pedestrian-Synthesis-GAN: generating pedestrian data in real scene and beyond. arXiv preprint arXiv: 1804.02047 (2018)."},{"key":"e_1_3_2_1_25_1","volume-title":"Proc. International Conference on Learning Representations (ICLR) arXiv:1511","author":"Radford Alec","year":"2016","unstructured":"Alec Radford , Luke Metz , and Soumith Chintala . 2016 . Unsupervised representation learning with deep convolutional generative adversarial networks . In Proc. International Conference on Learning Representations (ICLR) arXiv:1511 .06434. Alec Radford, Luke Metz, and Soumith Chintala. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks. In Proc. International Conference on Learning Representations (ICLR) arXiv:1511.06434."},{"key":"e_1_3_2_1_26_1","volume-title":"YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767","author":"Redmon Joseph","year":"2018","unstructured":"Joseph Redmon and Ali Farhadi . 2018. YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 ( 2018 ). Joseph Redmon and Ali Farhadi. 2018. YOLOv3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)."},{"key":"e_1_3_2_1_27_1","unstructured":"Scott E Reed Zeynep Akata Santosh Mohan Samuel Tenka Bernt Schiele and Honglak Lee. 2016. Learning what and where to draw. In Advances in Neural Information Processing Systems (NIPS). 217--225.  Scott E Reed Zeynep Akata Santosh Mohan Samuel Tenka Bernt Schiele and Honglak Lee. 2016. Learning what and where to draw. In Advances in Neural Information Processing Systems (NIPS). 217--225."},{"key":"e_1_3_2_1_28_1","unstructured":"Shaoqing Ren Kaiming He Ross Girshick and Jian Sun. 2015. Faster R-CNN: towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (NIPS). 91--99.  Shaoqing Ren Kaiming He Ross Girshick and Jian Sun. 2015. Faster R-CNN: towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (NIPS). 91--99."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Leonardo Rundo Changhee Han Yudai Nagano Jin Zhang Ryuichiro Hataya etal 2019. USE-Net: incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing (2019). (In press).  Leonardo Rundo Changhee Han Yudai Nagano Jin Zhang Ryuichiro Hataya et al. 2019. USE-Net: incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing (2019). (In press).","DOI":"10.1016\/j.neucom.2019.07.006"},{"key":"e_1_3_2_1_31_1","unstructured":"Tim Salimans Ian Goodfellow Wojciech Zaremba Vicki Cheung Alec Radford and Xi Chen. 2016. Improved techniques for training GANs. In Advances in Neural Information Processing Systems (NIPS). 2234--2242.  Tim Salimans Ian Goodfellow Wojciech Zaremba Vicki Cheung Alec Radford and Xi Chen. 2016. Improved techniques for training GANs. In Advances in Neural Information Processing Systems (NIPS). 2234--2242."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00536-8_1"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.241"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_2_1_35_1","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . J. Mach. Learn. Res. 9 (2008), 2579 -- 2605 . Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9 (2008), 2579--2605.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.23919\/MVA.2019.8757893"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00143"},{"key":"e_1_3_2_1_38_1","volume-title":"Proc. Computer Vision Problems in Plant Phenotyping (CVPPP). 324--334","author":"Zhu Yezi","year":"2018","unstructured":"Yezi Zhu , Marc Aoun , Marcel Krijn , Joaquin Vanschoren , and High Tech Campus . 2018 . Data augmentation using conditional generative adversarial networks for leaf counting in arabidopsis plants . In Proc. Computer Vision Problems in Plant Phenotyping (CVPPP). 324--334 . Yezi Zhu, Marc Aoun, Marcel Krijn, Joaquin Vanschoren, and High Tech Campus. 2018. Data augmentation using conditional generative adversarial networks for leaf counting in arabidopsis plants. In Proc. Computer Vision Problems in Plant Phenotyping (CVPPP). 324--334."}],"event":{"name":"CIKM '19: The 28th ACM International Conference on Information and Knowledge Management","location":"Beijing China","acronym":"CIKM '19","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 28th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3357384.3357890","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3357384.3357890","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:44:44Z","timestamp":1750203884000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3357384.3357890"}},"subtitle":["Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images"],"short-title":[],"issued":{"date-parts":[[2019,11,3]]},"references-count":39,"alternative-id":["10.1145\/3357384.3357890","10.1145\/3357384"],"URL":"https:\/\/doi.org\/10.1145\/3357384.3357890","relation":{},"subject":[],"published":{"date-parts":[[2019,11,3]]},"assertion":[{"value":"2019-11-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}