{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:04:05Z","timestamp":1761253445007,"version":"3.37.3"},"reference-count":24,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T00:00:00Z","timestamp":1582588800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"},{"start":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T00:00:00Z","timestamp":1582588800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11901436"],"award-info":[{"award-number":["11901436"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100014364","name":"Samsung Science and Technology Foundation","doi-asserted-by":"crossref","award":["SSTF-BA1402-01"],"award-info":[{"award-number":["SSTF-BA1402-01"]}],"id":[{"id":"10.13039\/501100014364","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["NRF-2017R1E1A1A03070653"],"award-info":[{"award-number":["NRF-2017R1E1A1A03070653"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Machine learning-based analysis of medical images often faces several hurdles, such as the lack of training data, the curse of the dimensionality problem, and generalization issues. One of the main difficulties is that there exists a computational cost problem in dealing with input data of large size matrices which represent medical images. The purpose of this paper is to introduce a framelet-pooling aided deep learning method for mitigating computational bundles caused by large dimensionality. By transforming high dimensional data into low dimensional components by filter banks and preserving detailed information, the proposed method aims to reduce the complexity of the neural network and computational costs significantly during the learning process. Various experiments show that our method is comparable to the standard unreduced learning method, while reducing computational burdens by decomposing large-sized learning tasks into several small-scale learning tasks.<\/jats:p>","DOI":"10.1088\/2632-2153\/ab592b","type":"journal-article","created":{"date-parts":[[2020,2,25]],"date-time":"2020-02-25T16:16:29Z","timestamp":1582647389000},"page":"015009","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Framelet pooling aided deep learning network: the method to process high dimensional medical data"],"prefix":"10.1088","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7072-7489","authenticated-orcid":false,"given":"Chang Min","family":"Hyun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang Cheol","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyun Cheol","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9546-753X","authenticated-orcid":false,"given":"Jae Kyu","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin Keun","family":"Seo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2020,2,25]]},"reference":[{"year":"2016","author":"Abadi","article-title":"TensorFlow: large-scale machine learning on heterogeneous systems","key":"mlstab592bbib8"},{"key":"mlstab592bbib1","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF00993164","article-title":"Approximation and estimation bounds for artificial neural networks","volume":"14","author":"Barron","year":"1994","journal-title":"Mach. Learn."},{"key":"mlstab592bbib2","doi-asserted-by":"publisher","first-page":"1872","DOI":"10.1109\/TPAMI.2012.230","article-title":"Invariant scattering convolution networks","volume":"35","author":"Bruna","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"mlstab592bbib3","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.acha.2013.10.001","article-title":"Data-driven tight frame construction and image denoising","volume":"37","author":"Cai","year":"2014","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"mlstab592bbib4","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1002\/cpa.3160410705","article-title":"Orthonormal bases of compactly supported wavelets","volume":"41","author":"Daubechies","year":"1988","journal-title":"Commun. Pure Appl. Math."},{"key":"mlstab592bbib6","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1117\/12.923203","article-title":"MAR-based wavelet frames and applications: image segmentation and surface reconstruction","volume":"8401","author":"Dong","year":"2012","journal-title":"Proc. SPIE"},{"key":"mlstab592bbib7","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1137\/16M1064969","article-title":"Image restoration: A general wavelet frame based model and its asymptotic analysis","volume":"49","author":"Dong","year":"2017","journal-title":"SIAM J. Math. Anal."},{"key":"mlstab592bbib10","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aac71a","article-title":"Deep learning for undersampled MRI reconstruction","volume":"63","author":"Hyun","year":"2018","journal-title":"Phys. Med. Biol."},{"year":"2015","author":"Ioffe","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","key":"mlstab592bbib11"},{"key":"mlstab592bbib12","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","article-title":"Deep convolutional neural network for inverse problems in imaging","volume":"26","author":"Jin","year":"2017","journal-title":"IEEE Trans. Image Process."},{"year":"2014","author":"Kingma","article-title":"Adam: a method for stochastic optimization","key":"mlstab592bbib13"},{"key":"mlstab592bbib14","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TITB.2010.2091279","article-title":"Multi-scale amplitude modulation-frequency modulation (AM-FM) texture analysis of multiple sclerosis in brain MRI images,","volume":"15","author":"Loizou","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"mlstab592bbib16","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-374370-1.X0001-8","author":"Mallat","year":"2009","edition":"3rd edn"},{"key":"mlstab592bbib15","doi-asserted-by":"publisher","DOI":"10.1098\/rsta.2015.0203","article-title":"Understanding deep convolutional networks","volume":"374","author":"Mallat","year":"2016","journal-title":"Philos. Trans. R. Soc. A"},{"key":"mlstab592bbib17","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1142\/S0219530516400042","article-title":"Deep versus Shallow networks: an approximation theory perspective","volume":"14","author":"Mhaskar","year":"2016","journal-title":"Anal. Appl."},{"year":"2010","author":"Nishimura","key":"mlstab592bbib18"},{"year":"2013","author":"Pascanu","article-title":"On the number of response regions of deep feedforward networks with piecewise linear activations","key":"mlstab592bbib19"},{"key":"mlstab592bbib20","first-page":"408","article-title":"Affine System in L 2 ( R d ) : the analysis of the analysis operator","volume":"148","author":"Ron","year":"1997","journal-title":"J. Fourier Anal. Appl."},{"key":"mlstab592bbib21","doi-asserted-by":"publisher","first-page":"9351","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"mlstab592bbib22","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","article-title":"A deep cascade of convolutional neural networks for dynamic MR image reconstruction","volume":"37","author":"Schlemper","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"year":"2013","author":"Seo","doi-asserted-by":"publisher","key":"mlstab592bbib23","DOI":"10.1002\/9781118478141"},{"key":"mlstab592bbib24","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1137\/110860604","article-title":"B-spline framelets derived from the unitary extension principle","volume":"45","author":"Shen","year":"2013","journal-title":"Soc. Ind. Appl. Math."},{"key":"mlstab592bbib25","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"mlstab592bbib26","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1137\/17M1141771","article-title":"Deep convolutional framelets: a general deep learning framework for inverse problems","volume":"11","author":"Ye","year":"2018","journal-title":"SIAM J. Imaging Sci."}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b","content-type":"text\/html","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T15:58:28Z","timestamp":1637769508000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ab592b"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,25]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,2,25]]},"published-print":{"date-parts":[[2020,3,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ab592b","relation":{},"ISSN":["2632-2153"],"issn-type":[{"type":"electronic","value":"2632-2153"}],"subject":[],"published":{"date-parts":[[2020,2,25]]},"assertion":[{"value":"Framelet pooling aided deep learning network: the method to process high dimensional medical data","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2020 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2019-07-21","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2019-11-19","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2020-02-25","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}