{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T05:44:59Z","timestamp":1761975899864,"version":"3.28.0"},"reference-count":44,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,10]]},"DOI":"10.1109\/icpr48806.2021.9412150","type":"proceedings-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T02:15:54Z","timestamp":1620267354000},"page":"6051-6058","source":"Crossref","is-referenced-by-count":4,"title":["Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation"],"prefix":"10.1109","author":[{"given":"Martin","family":"Kolarik","sequence":"first","affiliation":[]},{"given":"Radim","family":"Burget","sequence":"additional","affiliation":[]},{"given":"Carlos M.","family":"Travieso-Gonzalez","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Kocica","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759329"},{"journal-title":"Github repository containing source code to this paper","year":"0","author":"kolarik","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_14"},{"key":"ref31","first-page":"37","article-title":"Nabla-net: a deep dag-like convolutional architecture for biomedical image segmentation: application to white-matter lesion segmentation in multiple sclerosis","author":"mckinley","year":"0","journal-title":"MSSEG Challenge Proceedings Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure"},{"key":"ref30","article-title":"Handling unbalanced data in deep image segmentation","author":"small","year":"2017","journal-title":"University of Colorado"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"ref36","article-title":"Multi-scale context aggregation by dilated convolutions","author":"yu","year":"2015","journal-title":"ArXiv Preprint"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/app9030404"},{"key":"ref34","article-title":"Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program)","author":"pineau","year":"2020","journal-title":"ArXiv Preprint"},{"key":"ref10","article-title":"U-net: Convolutional networks for biomedical image segmentation","volume":"abs 1505 4597","author":"ronneberger","year":"2015","journal-title":"CoRR"},{"key":"ref40","article-title":"TensorFlow: Large-scale machine learning on heterogeneous systems","author":"abadi","year":"2015","journal-title":"software available from tensorflow org"},{"key":"ref11","article-title":"Object-contextual representations for semantic segmentation","author":"yuan","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.23919\/FRUCT.2018.8588071"},{"key":"ref13","article-title":"Improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function","author":"cai","year":"2017","journal-title":"ArXiv Preprint"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2019.102901"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101781"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.59"},{"key":"ref19","article-title":"Med3d: Transfer learning for 3d medical image analysis","author":"chen","year":"2019","journal-title":"ArXiv Preprint"},{"journal-title":"Segmentation models","year":"2019","author":"yakubovskiy","key":"ref28"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"journal-title":"The ITK Software Guide Insight Toolkit","year":"2015","author":"johnson","key":"ref3"},{"journal-title":"Deep Learning","year":"2016","author":"goodfellow","key":"ref6"},{"key":"ref29","first-page":"3347","article-title":"Transfusion: Understanding transfer learning for medical imaging","author":"raghu","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref8","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"ArXiv Preprint"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00100"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00909"},{"key":"ref9","article-title":"Deep semantic segmentation of natural and medical images: A review","author":"taghanaki","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-31911-7"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33676-9_26"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2832217"},{"key":"ref42","article-title":"Ms lesion segmentation using flair mri only","author":"knight","year":"0","journal-title":"Proceedings of the 1st MICCAI Challenge on Multiple Sclerosis Lesions Segmentation Challenge-MICCAI-MSSEG"},{"key":"ref24","article-title":"Thick-ened 2d networks for 3d medical image segmentation","author":"yu","year":"2019","journal-title":"ArXiv Preprint"},{"journal-title":"Keras","year":"2015","author":"chollet","key":"ref41"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_94"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2886371"},{"key":"ref26","article-title":"Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation","author":"iglovikov","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref43","article-title":"Automatic multiple sclerosis lesion segmentation using hybrid artificial neural networks","volume":"29","author":"mahbod","year":"0","journal-title":"MSSEG Challenge Proceedings Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure"},{"key":"ref25","first-page":"arxiv-1911","article-title":"Reinventing 2d convolutions for 3d images","author":"yang","year":"2019","journal-title":"ArXiv"}],"event":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2021,1,10]]},"location":"Milan, Italy","end":{"date-parts":[[2021,1,15]]}},"container-title":["2020 25th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9411940\/9411911\/09412150.pdf?arnumber=9412150","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:40:46Z","timestamp":1652197246000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9412150\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/icpr48806.2021.9412150","relation":{},"subject":[],"published":{"date-parts":[[2021,1,10]]}}}