{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T02:52:53Z","timestamp":1776394373645,"version":"3.51.2"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030203504","type":"print"},{"value":"9783030203511","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20351-1_3","type":"book-chapter","created":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T15:53:24Z","timestamp":1558540404000},"page":"29-41","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Semi-supervised and Task-Driven Data Augmentation"],"prefix":"10.1007","author":[{"given":"Krishna","family":"Chaitanya","sequence":"first","affiliation":[]},{"given":"Neerav","family":"Karani","sequence":"additional","affiliation":[]},{"given":"Christian F.","family":"Baumgartner","sequence":"additional","affiliation":[]},{"given":"Anton","family":"Becker","sequence":"additional","affiliation":[]},{"given":"Olivio","family":"Donati","sequence":"additional","affiliation":[]},{"given":"Ender","family":"Konukoglu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,22]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Andermatt, S., Horv\u00e1th, A., Pezold, S., Cattin, P.: Pathology segmentation using distributional differences to images of healthy origin. arXiv preprint \n                      arXiv:1805.10344\n                      \n                     (2018)","DOI":"10.1007\/978-3-030-11723-8_23"},{"key":"3_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-319-66185-8_29","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"W Bai","year":"2017","unstructured":"Bai, W., et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 253\u2013260. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-66185-8_29"},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., et al.: Deep learningtechniques for automatic MRI cardiac multi-structures segmentation anddiagnosis: is the problem solved? IEEE Trans. Med. Imag. 37, 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imag."},{"key":"3_CR4","unstructured":"Bowles, C., et al.: GAN augmentation: augmenting training data using generative adversarial networks. arXiv preprint \n                      arXiv:1810.10863\n                      \n                     (2018)"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Can, Y., Chaitanya, K., Mustafa, B., Koch, L., Konukoglu, E., Baumgartner, C.: Learning to segment medical images with scribble-supervision alone. arXiv preprint \n                      arXiv:1807.04668v1\n                      \n                     (2018)","DOI":"10.1007\/978-3-030-00889-5_27"},{"key":"3_CR6","unstructured":"Cire\u015fan, D.C., Meier, U., Masci, J., Gambardella, L.M., Schmidhuber, J.: High-performance neural networks for visual object classification. arXiv preprint \n                      arXiv:1102.0183\n                      \n                     (2011)"},{"issue":"3","key":"3_CR7","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1109\/TMI.2017.2759102","volume":"37","author":"P Costa","year":"2018","unstructured":"Costa, P., et al.: End-to-end adversarial retinal image synthesis. IEEE Trans. Med. Imag. 37(3), 781\u2013791 (2018)","journal-title":"IEEE Trans. Med. Imag."},{"key":"3_CR8","unstructured":"Eaton-Rosen, Z., Bragman, F., Ourselin, S., Cardoso, M.J.: Improving data augmentation for medical image segmentation. In: International Conference on Medical Imaging with Deep Learning (2018)"},{"key":"3_CR9","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"3_CR10","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint \n                      arXiv:1503.02531\n                      \n                     (2015)"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Hong, J., Park, B.y., Park, H.: Convolutional neural network classifier for distinguishing Barrett\u2019s esophagus and neoplasia endomicroscopy images. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC, pp. 2892\u20132895. IEEE (2017)","DOI":"10.1109\/EMBC.2017.8037461"},{"key":"3_CR12","unstructured":"Luc, P., Couprie, C., Chintala, S., Verbeek, J.: Semantic segmentation using adversarial networks. arXiv preprint \n                      arXiv:1611.08408\n                      \n                     (2016)"},{"key":"3_CR13","unstructured":"Oliver, A., Odena, A., Raffel, C., Cubuk, E.D., Goodfellow, I.J.: Realistic evaluation of deep semi-supervised learning algorithms. arXiv preprint \n                      arXiv:1804.09170\n                      \n                     (2018)"},{"key":"3_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/978-3-030-01201-4_33","volume-title":"OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis","author":"F Perez","year":"2018","unstructured":"Perez, F., Vasconcelos, C., Avila, S., Valle, E.: Data augmentation for skin lesion analysis. In: Stoyanov, D., et al. (eds.) CARE\/CLIP\/OR 2.0\/ISIC-2018. LNCS, vol. 11041, pp. 303\u2013311. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-01201-4_33"},{"key":"3_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). \n                      https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"3_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-00536-8_1","volume-title":"Simulation and Synthesis in Medical Imaging","author":"H-C Shin","year":"2018","unstructured":"Shin, H.-C., et al.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, A., Goksel, O., Oguz, I., Burgos, N. (eds.) SASHIMI 2018. LNCS, vol. 11037, pp. 1\u201311. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-030-00536-8_1"},{"key":"3_CR17","unstructured":"Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Seventh International Conference on Document Analysis and Recognition (ICDAR), vol. 2, pp. 958\u2013963 (2003)"},{"issue":"6","key":"3_CR18","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imag. 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imag."},{"key":"3_CR19","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint \n                      arXiv:1710.09412\n                      \n                     (2017)"},{"key":"3_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1007\/978-3-319-66179-7_47","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"Y Zhang","year":"2017","unstructured":"Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 408\u2013416. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-66179-7_47"}],"container-title":["Lecture Notes in Computer Science","Information Processing in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20351-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T15:53:41Z","timestamp":1558540421000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20351-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030203504","9783030203511"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20351-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmi2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmi2019.cse.ust.hk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}