{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T15:57:06Z","timestamp":1778601426802,"version":"3.51.4"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AI Singapore Programme Award","award":["AISG-GC-2019-001"],"award-info":[{"award-number":["AISG-GC-2019-001"]}]},{"name":"AI Singapore Programme Award","award":["AISG-GC-2019-002"],"award-info":[{"award-number":["AISG-GC-2019-002"]}]},{"name":"NMRC Health Service Research","award":["MOH-000030-00"],"award-info":[{"award-number":["MOH-000030-00"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1109\/jbhi.2021.3081355","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T19:35:02Z","timestamp":1621366502000},"page":"2388-2397","source":"Crossref","is-referenced-by-count":45,"title":["Interpretable and Lightweight 3-D Deep Learning Model for Automated ACL Diagnosis"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4948-083X","authenticated-orcid":false,"given":"YoungSeok","family":"Jeon","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kensuke","family":"Yoshino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigeo","family":"Hagiwara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsuya","family":"Watanabe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Swee Tian","family":"Quek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7561-7492","authenticated-orcid":false,"given":"Hiroshi","family":"Yoshioka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5338-6248","authenticated-orcid":false,"given":"Mengling","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref38","first-page":"3","article-title":"CBAM: Convolutional block attention module","author":"woo","year":"2018","journal-title":"In Proc European Conf Comp Vis"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1806905115"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06167-y"},{"key":"ref31","article-title":"MURA: Large dataset for abnormality detection in musculoskeletal radiographs","author":"rajpurkar","year":"2017"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01867-1"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref36","article-title":"Local interpretable model-agnostic explanations for classification of lymph node metastases","volume":"19","author":"sousa","year":"2019","journal-title":"SENSORS"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-45415-5"},{"key":"ref34","article-title":"q-space deep learning for Alzheimer's disease diagnosis: Global prediction and weakly-supervised localization","volume":"1580","author":"golkov","year":"2018","journal-title":"Proc 27th Annu Meeting ISMRM"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3390\/s21020455"},{"key":"ref2","article-title":"Knee injury detection using mri with efficiently-layered network (ELNET)","author":"tsai","year":"2020"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002699"},{"key":"ref20","first-page":"233","article-title":"A closer look at memorization in deep networks","author":"arpit","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref22","article-title":"Language models are few-shot learners","author":"brown","year":"2020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"ref23","article-title":"WaveNet: A generative model for raw","author":"oord","year":"2016"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04051-w"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref51","first-page":"9605","article-title":"An intriguing failing of convolutional neural networks and the coordconv solution","author":"liu","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref58","article-title":"Improving MRI-based knee disorder diagnosis with pyramidal feature details","author":"dunnhofer","year":"2021","journal-title":"Proc Fourth Conf Medical Imaging Deep Learn (MIDL)"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF44664.2019.9048671"},{"key":"ref56","article-title":"Efficient detection of knee anterior cruciate ligament from magnetic resonance imaging using deep learning approach","volume":"11","author":"awan","year":"2021","journal-title":"Diagnostics"},{"key":"ref55","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_19"},{"key":"ref53","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref52","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"0","journal-title":"Proc Conf North American Chapter Association Comput Linguistics Human Lang Tech Volume 1 (Long and Short Papers)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref11","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 0.5mb model size","author":"iandola","year":"2016"},{"key":"ref40","article-title":"Progressive attention networks for visual attribute prediction","author":"seo","year":"2016"},{"key":"ref12","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017"},{"key":"ref13","article-title":"A Survey on federated learning systems: Vision, hype and reality for data privacy and protection","author":"li","year":"2019"},{"key":"ref14","article-title":"Bandwidth slicing to boost federated learning in edge computing","author":"li","year":"2019","journal-title":"ArXiv"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Computer Vision-ECCV 2014"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref19","article-title":"Understanding deep learning requires rethinking generalization","author":"zhang","year":"2016"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-019-00193-4"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5312\/wjo.v2.i8.75"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3233547.3233667"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2019180091"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref49","article-title":"Speeding-up convolutional neural networks using fine-tuned cp-decomposition","author":"lebedev","year":"2014"},{"key":"ref9","article-title":"Grad-CAM: Generalized gradient-based visual explanations for deep convolutional networks","author":"chattopadhyay","year":"2017"},{"key":"ref46","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref45","article-title":"An attention-based multi-resolution model for prostate whole slide imageclassification and localization","author":"li","year":"2019"},{"key":"ref48","article-title":"Instance normalization: The missing ingredient for fast stylization","author":"ulyanov","year":"2016"},{"key":"ref47","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref42","article-title":"Attention U-Net: Learning where to look for the pancreas","author":"oktay","year":"2018"},{"key":"ref41","article-title":"Learn to pay attention","author":"jetley","year":"0","journal-title":"Proc 6th Int Conf Learn Representations"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2971383"},{"key":"ref43","article-title":"Attention-gated networks for improving ultrasound scan plane detection","author":"schlemper","year":"0","journal-title":"Medical Image Comput Comput -Assisted Intervention MICCAI&#x2014;Int Conf Medical Image Comput Comput -Assisted Intervention"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/9497060\/09435063.pdf?arnumber=9435063","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:39Z","timestamp":1639770999000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9435063\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":58,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2021.3081355","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}