{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:11:01Z","timestamp":1792080661895,"version":"4.4.0"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST\u2010107\u20102314\u2010B\u2010011 \u2010002 \u2010MY3"],"award-info":[{"award-number":["MOST\u2010107\u20102314\u2010B\u2010011 \u2010002 \u2010MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST\u2010107\u20102314\u2010B\u2010039\u2010071"],"award-info":[{"award-number":["MOST\u2010107\u20102314\u2010B\u2010039\u2010071"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST\u2010108\u20102314\u2010B\u2010039\u2010014"],"award-info":[{"award-number":["MOST\u2010108\u20102314\u2010B\u2010039\u2010014"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 107\u20102314\u2010B\u2010011\u2010002\u2010MY3, MOST\u2010107\u20102314\u2010B\u2010039\u2010071 and MOST\u2010108\u20102314\u2010B\u2010039\u2010014"],"award-info":[{"award-number":["MOST 107\u20102314\u2010B\u2010011\u2010002\u2010MY3, MOST\u2010107\u20102314\u2010B\u2010039\u2010071 and MOST\u2010108\u20102314\u2010B\u2010039\u2010014"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["NMR in Biomedicine"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>\n                    Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to\n                    <jats:italic>T<\/jats:italic>\n                    <jats:sub>2<\/jats:sub>\n                    \u2010weighted, postcontrast\n                    <jats:italic>T<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    \u2010weighted, and diffusion\u2010weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two\u2010dimensional convolution neural network, U\u2010Net, to segment and classify parotid gland tumors. The U\u2010Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion\u2010related parameters performed better than those with structural MR images. The performance results (\n                    <jats:italic>n<\/jats:italic>\n                    = 85) of the diffusion\u2010based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion\u2010weighted and contrast\u2010enhanced\n                    <jats:italic>T<\/jats:italic>\n                    <jats:sub>1<\/jats:sub>\n                    \u2010weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.\n                  <\/jats:p>","DOI":"10.1002\/nbm.4408","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T19:49:40Z","timestamp":1599248980000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Classification of parotid gland tumors by using multimodal MRI and deep learning"],"prefix":"10.1002","volume":"34","author":[{"given":"Yi\u2010Ju","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering National Taiwan University of Science and Technology  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6836-3946","authenticated-orcid":false,"given":"Teng\u2010Yi","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering National Taiwan University of Science and Technology  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5865-6836","authenticated-orcid":false,"given":"Yi\u2010Jui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Automatic Control Engineering Feng Chia University  Taichung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsiao\u2010Wen","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering National Taiwan University  Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5219-0406","authenticated-orcid":false,"given":"Chun\u2010Jung","family":"Juan","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging China Medical University Hsinchu Hospital  Hsinchu Taiwan"},{"name":"Department of Radiology, School of Medicine, College of Medicine China Medical University  Taichung Taiwan"},{"name":"Department of Medical Imaging China Medical University Hospital  Taichung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1590\/1678-775720160214"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1309\/AJCPOIE0CZNAT6SQ"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2015.04.003"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2017.05.020"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.soc.2015.03.012"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nic.2018.01.009"},{"issue":"6","key":"e_1_2_9_8_1","first-page":"1081","article-title":"The role of diffusion\u2010weighted imaging in patients with brain tumors","volume":"22","author":"Kono K","year":"2001","journal-title":"Am J Neuroradiol"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nic.2018.01.010"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2012.04.025"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.A1412"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1055\/s-2005-858297"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.17.18492"},{"issue":"1","key":"e_1_2_9_14_1","first-page":"116","article-title":"Apparent diffusion coefficient mapping of salivary gland tumors: prediction of the benignancy and malignancy","volume":"28","author":"Eida S","year":"2007","journal-title":"Am J Neuroradiol"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20150912"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clbc.2019.11.009"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26244"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06214-8"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2886276"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06205-9"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019182012"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2531082228"},{"key":"e_1_2_9_27_1","doi-asserted-by":"crossref","unstructured":"RonnebergerO FischerP BroxT.U\u2010net: convolutional networks for biomedical image segmentation. 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