{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:03:00Z","timestamp":1778630580627,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s11227-020-03164-7","type":"journal-article","created":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T11:03:09Z","timestamp":1579518189000},"page":"7315-7332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Development of training environment for deep learning with medical images on supercomputer system based on asynchronous parallel Bayesian optimization"],"prefix":"10.1007","volume":"76","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6471-9936","authenticated-orcid":false,"given":"Yukihiro","family":"Nomura","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Issei","family":"Sato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toshihiro","family":"Hanawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shouhei","family":"Hanaoka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takahiro","family":"Nakao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomomi","family":"Takenaga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tetsuya","family":"Hoshino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuji","family":"Sekiya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soichiro","family":"Miki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takeharu","family":"Yoshikawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoto","family":"Hayashi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Osamu","family":"Abe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,20]]},"reference":[{"key":"3164_CR1","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M (2016) TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv e-prints arXiv:1603.04467"},{"issue":"2","key":"3164_CR2","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"RSG Armato","year":"2011","unstructured":"Armato RSG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beeke EJ, Yankelevitz D, Biancardi AM, Bland PH, Brown MS, Engelmann RM, Laderach GE, Max D, Pais RC, Qing DP, Roberts RY, Smith AR, Starkey A, Batrah P, Caligiuri P, Farooqi A, Gladish GW, Jude CM, Munden RF, Petkovska I, Quint LE, Schwartz LH, Sundaram B, Dodd LE, Fenimore C, Gur D, Petrick N, Freymann J, Kirby J, Hughes B, Casteele AV, Gupte S, Sallamm M, Heath MD, Kuhn MH, Dharaiya E, Burns R, Fryd DS, Salganicoff M, Anand V, Shreter U, Vastagh S, Croft BY (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on ct scans. Med Phys 38(2):915\u201331. https:\/\/doi.org\/10.1118\/1.3528204","journal-title":"Med Phys"},{"key":"3164_CR3","doi-asserted-by":"crossref","unstructured":"Balaprakash P, Salim M, Uram TD, Vishwanath V, Wild SM (2018) Deephyper: asynchronous hyperparameter search for deep neural networks. In: 2018 IEEE 25th international conference on high performance computing (HiPC), pp 42\u201351","DOI":"10.1109\/HiPC.2018.00014"},{"issue":"Feb","key":"3164_CR4","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(Feb):281\u2013305","journal-title":"J Mach Learn Res"},{"issue":"8","key":"3164_CR5","doi-asserted-by":"publisher","first-page":"2313","DOI":"10.1118\/1.1769352","volume":"31","author":"DP Chakraborty","year":"2004","unstructured":"Chakraborty DP, Berbaum KS (2004) Observer studies involving detection and localization: modeling, analysis, and validation. Med Phys 31(8):2313\u201330. https:\/\/doi.org\/10.1118\/1.1769352","journal-title":"Med Phys"},{"key":"3164_CR6","unstructured":"Chollet F (2015) Keras. https:\/\/github.com\/fchollet\/keras. Accessed 19 Jan 2020"},{"key":"3164_CR7","unstructured":"Contal E, Perchet V, Vayatis N (2014) Gaussian process optimization with mutual information. In: Proceedings of the 31st international conference on machine learning, vol\u00a032, pp 253\u2013261"},{"issue":"6","key":"3164_CR8","doi-asserted-by":"publisher","first-page":"W809","DOI":"10.2214\/ajr.12.9673","volume":"201","author":"P Gerard","year":"2013","unstructured":"Gerard P, Kapadia N, Chang PT, Acharya J, Seiler M, Lefkovitz Z (2013) Extended outlook: description, utilization, and daily applications of cloud technology in radiology. AJR Am J Roentgenol 201(6):W809\u201311. https:\/\/doi.org\/10.2214\/ajr.12.9673","journal-title":"AJR Am J Roentgenol"},{"key":"3164_CR9","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv e-prints arXiv:1512.03385"},{"key":"3164_CR10","unstructured":"Hiraishi T, Abe T, Iwashita T, Nakashima H (2012) Xcrypt: a perl extension for job level parallel programming. In: Proceedings of the WHIST 2012"},{"issue":"7","key":"3164_CR11","doi-asserted-by":"publisher","first-page":"070901","DOI":"10.1118\/1.4811272","volume":"40","author":"GC Kagadis","year":"2013","unstructured":"Kagadis GC, Kloukinas C, Moore K, Philbin J, Papadimitroulas P, Alexakos C, Nagy PG, Visvikis D, Hendee WR (2013) Cloud computing in medical imaging. Med Phys 40(7):070901. https:\/\/doi.org\/10.1118\/1.4811272","journal-title":"Med Phys"},{"key":"3164_CR12","unstructured":"Kandasamy K, Krishnamurthy A, Schneider J, Poczos B (2018) Parallelised Bayesian optimisation via Thompson sampling. In: Proceedings of the 21st international conference on artificial intelligence and statistics, pp 133\u2013142"},{"key":"3164_CR13","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv e-prints arXiv:1412.6980"},{"key":"3164_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"key":"3164_CR15","doi-asserted-by":"publisher","first-page":"621","DOI":"10.4018\/978-1-4666-3994-2.ch032","volume-title":"Image processing: concepts, methodologies, tools, and applications","author":"Y Masutani","year":"2013","unstructured":"Masutani Y, Nemoto M, Nomura Y, Hayashi N (2013) Clinical machine learning in action: cad system design, development, tuning, and long-term experience. In: Suzuki K (ed) Image processing: concepts, methodologies, tools, and applications. IGI Global, Philadelphia, pp 621\u2013638"},{"issue":"6","key":"3164_CR16","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.jacr.2006.02.021","volume":"3","author":"CE Metz","year":"2006","unstructured":"Metz CE (2006) Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. J Am Coll Radiol 3(6):413\u201322. https:\/\/doi.org\/10.1016\/j.jacr.2006.02.021","journal-title":"J Am Coll Radiol"},{"key":"3164_CR17","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. arXiv e-prints arXiv:1606.04797","DOI":"10.1109\/3DV.2016.79"},{"key":"3164_CR18","first-page":"117","volume":"2","author":"J Mockus","year":"1978","unstructured":"Mockus J, Tiesis V, Zilinskas A (1978) The application of Bayesian methods for seeking the extremum. Towards Glob Optim 2:117\u2013129","journal-title":"Towards Glob Optim"},{"key":"3164_CR19","doi-asserted-by":"crossref","unstructured":"Neary P (2018) Automatic hyperparameter tuning in deep convolutional neural networks using asynchronous reinforcement learning. In: 2018 IEEE international conference on cognitive computing (ICCC), pp 73\u201377","DOI":"10.1109\/ICCC.2018.00017"},{"issue":"4","key":"3164_CR20","doi-asserted-by":"publisher","first-page":"12","DOI":"10.5430\/jbgc.v4n4p12","volume":"4","author":"Y Nomura","year":"2014","unstructured":"Nomura Y, Masutani Y, Miki S, Nemoto M, Hanaoka S, Yoshikawa T, Hayashi N, Ohtomo K (2014) Performance improvement in computerized detection of cerebral aneurysms by retraining classifier using feedback data collected in routine reading environment. J Biomed Graph Comput 4(4):12\u201321. https:\/\/doi.org\/10.5430\/jbgc.v4n4p12","journal-title":"J Biomed Graph Comput"},{"issue":"3","key":"3164_CR21","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/s11604-018-0784-6","volume":"37","author":"Y Nomura","year":"2019","unstructured":"Nomura Y, Hayashi N, Hanaoka S, Takenaga T, Nemoto M, Miki S, Yoshikawa T, Abe O (2019) Can the spherical gold standards be used as an alternative to painted gold standards for the computerized detection of lesions using voxel-based classification? Jpn J Radiol 37(3):264\u2013273. https:\/\/doi.org\/10.1007\/s11604-018-0784-6","journal-title":"Jpn J Radiol"},{"key":"3164_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. arXiv e-prints arXiv:1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"3164_CR23","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1002\/mp.13264","volume":"46","author":"B Sahiner","year":"2019","unstructured":"Sahiner B, Pezeshk A, Hadjiiski LM, Wang X, Drukker K, Cha KH, Summers RM, Giger ML (2019) Deep learning in medical imaging and radiation therapy. Med Phys 46(1):e1\u2013e36. https:\/\/doi.org\/10.1002\/mp.13264","journal-title":"Med Phys"},{"key":"3164_CR24","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, pp 2951\u20132959"},{"key":"3164_CR25","unstructured":"Srinivas N, Krause A, Kakade S, Seeger M (2010) Gaussian process optimization in the bandit setting: no regret and experimental design. In: Proceedings of the 27th international conference on machine learning, Omnipress, USA, ICML\u201910, pp 1015\u20131022, http:\/\/dl.acm.org\/citation.cfm?id=3104322.3104451"},{"key":"3164_CR26","unstructured":"Tokui S, Oono K, Hido S, Clayton J (2015) Chainer: a next-generation open source framework for deep learning. In: Proceedings of workshop on machine learning systems (learningsys) in the 29th annual conference on neural information processing systems (NIPS), vol\u00a05, pp 1\u20136"},{"issue":"6","key":"3164_CR27","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1118\/1.598277","volume":"25","author":"O Tsujii","year":"1998","unstructured":"Tsujii O, Freedman MT, Mun SK (1998) Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network. Med Phys 25(6):998\u20131007. https:\/\/doi.org\/10.1118\/1.598277","journal-title":"Med Phys"},{"issue":"1","key":"3164_CR28","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s11604-018-0795-3","volume":"37","author":"D Ueda","year":"2019","unstructured":"Ueda D, Shimazaki A, Miki Y (2019) Technical and clinical overview of deep learning in radiology. Jpn J Radiol 37(1):15\u201333. https:\/\/doi.org\/10.1007\/s11604-018-0795-3","journal-title":"Jpn J Radiol"},{"issue":"18","key":"3164_CR29","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1186\/s12859-018-2508-4","volume":"19","author":"JM Wozniak","year":"2018","unstructured":"Wozniak JM, Jain R, Balaprakash P, Ozik J, Collier NT, Bauer J, Xia F, Brettin T, Stevens R, Mohd-Yusof J, Cardona CG, Baughman BVEM (2018) Candle\/supervisor: a workflow framework for machine learning applied to cancer research. BMC Bioinform 19(18):491. https:\/\/doi.org\/10.1186\/s12859-018-2508-4","journal-title":"BMC Bioinform"},{"issue":"2","key":"3164_CR30","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1166\/jmihi.2015.1382","volume":"5","author":"G Wu","year":"2015","unstructured":"Wu G, Zhang X, Luo S, Hu Q (2015) Lung segmentation based on customized active shape model from digital radiography chest images. J Med Imaging Health Info 5(2):184\u2013191. https:\/\/doi.org\/10.1166\/jmihi.2015.1382","journal-title":"J Med Imaging Health Info"},{"issue":"4","key":"3164_CR31","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s11604-018-0726-3","volume":"36","author":"K Yasaka","year":"2018","unstructured":"Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O (2018) Deep learning with convolutional neural network in radiology. Jpn J Radiol 36(4):257\u2013272. https:\/\/doi.org\/10.1007\/s11604-018-0726-3","journal-title":"Jpn J Radiol"},{"key":"3164_CR32","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2017) Random erasing data augmentation. arXiv e-prints arXiv:1708.04896"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03164-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11227-020-03164-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03164-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:43:06Z","timestamp":1611016986000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11227-020-03164-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,20]]},"references-count":32,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["3164"],"URL":"https:\/\/doi.org\/10.1007\/s11227-020-03164-7","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,20]]},"assertion":[{"value":"20 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}