{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:08:39Z","timestamp":1776978519811,"version":"3.51.4"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"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":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1109\/tmi.2020.3047598","type":"journal-article","created":{"date-parts":[[2020,12,28]],"date-time":"2020-12-28T20:47:57Z","timestamp":1609188477000},"page":"2759-2770","source":"Crossref","is-referenced-by-count":61,"title":["Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT"],"prefix":"10.1109","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0034-9013","authenticated-orcid":false,"given":"Ke","family":"Yan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7614-4524","authenticated-orcid":false,"given":"Jinzheng","family":"Cai","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4825-7046","authenticated-orcid":false,"given":"Youjing","family":"Zheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3315-1772","authenticated-orcid":false,"given":"Adam P.","family":"Harrison","sequence":"additional","affiliation":[]},{"given":"Dakai","family":"Jin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8719-3375","authenticated-orcid":false,"given":"Youbao","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6452-2751","authenticated-orcid":false,"given":"Yuxing","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0561-1615","authenticated-orcid":false,"given":"Lingyun","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Le","family":"Lu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.12.002"},{"key":"ref38","article-title":"A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises","author":"kevin zhou","year":"2020","journal-title":"arXiv 2008 09104"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00433"},{"key":"ref32","first-page":"1","article-title":"Distilling the Knowledge in a Neural Network","author":"hinton","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","first-page":"1","article-title":"Continual learning for domain adaptation in chest X-ray classification","author":"lenga","year":"2020","journal-title":"Proc Med Image Deep Learn"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759478"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"jialin pan","year":"2010","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-31723-2_25"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_19"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00667"},{"key":"ref28","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","article-title":"The liver tumor segmentation benchmark (LiTS)","author":"bilic","year":"2019"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref2","first-page":"291","article-title":"3D U&#x00B2;-Net: A 3D universal U-Net for multi-domain medical image segmentation","author":"huang","year":"2019","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693"},{"key":"ref20","article-title":"Detecting scatteredly-distributed, small, and critically important objects in 3D oncology imaging via decision stratification","author":"zhu","year":"2020","journal-title":"arXiv 2005 13705"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_58"},{"key":"ref21","year":"2020","journal-title":"RadReport Template Library"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_2"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00746"},{"key":"ref26","year":"2016","journal-title":"CT Lymph Nodes Dataset&#x2014;The Cancer Imaging Archive (TCIA) Public Access"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_22"},{"key":"ref50","article-title":"Objects as points","author":"zhou","year":"2019","journal-title":"arXiv 1904 07850"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00093"},{"key":"ref59","year":"2020","journal-title":"Lung Nodule Analysis 2016 Results"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref57","article-title":"On the variance of the adaptive learning rate and beyond","author":"liu","year":"2019","journal-title":"arXiv 1908 03265"},{"key":"ref56","author":"massa","year":"2018","journal-title":"Maskrcnn-Benchmark Fast Modular Reference Implementation of Instance Segmentation and Object Detection Algorithms in Pytorch"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00872"},{"key":"ref54","author":"yang","year":"2019","journal-title":"Reinventing 2D Convolutions for 3D Medical Images"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00965"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2008.10.026"},{"key":"ref40","first-page":"257","article-title":"JSSR: A joint synthesis, segmentation, and registration system for 3D multi-modal image alignment of large-scale pathological CT scans","author":"liu","year":"2020","journal-title":"Proc ECCV"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13264"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_90"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2613502"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_20"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2482920"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3000949"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01077"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00973"},{"key":"ref5","first-page":"1","article-title":"On the limits of cross-domain generalization in automated X-ray prediction","author":"cohen","year":"2020","journal-title":"Proc 3rd Conf Med Imag Deep Learn"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref7","first-page":"757","article-title":"Learning to recognize abnormalities in chest X-rays with location-aware dense networks","volume":"11401","author":"g\u00fcndel","year":"2019","journal-title":"Proc Iberoamer Cong Pattern Recog"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.3.036501"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00401"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00847"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101766"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_27"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2995518"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00020"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/9552972\/09309244.pdf?arnumber=9309244","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:49:18Z","timestamp":1652194158000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9309244\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10]]},"references-count":59,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2020.3047598","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10]]}}}