{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T19:13:16Z","timestamp":1735585996164},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"10","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106191"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Ningxia Hui Nationality Autonomous Region","award":["2022BEG02025"]},{"DOI":"10.13039\/501100015401","name":"Key Research and Development Program of Shaanxi Province","doi-asserted-by":"publisher","award":["2021GXLH-Z-095"],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019069","name":"Consulting Research Project of the Chinese Academy of Engineering (The Online and Offline Mixed Educational Service System for The Belt and Road Training in Massive Open Online Courses","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100019069","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of China Knowledge Centre for Engineering Science and Technology"},{"name":"Innovation Team from the Ministry of Education","award":["IRT_17R86"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1109\/tmi.2023.3273236","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T17:33:28Z","timestamp":1683308008000},"page":"3000-3011","source":"Crossref","is-referenced-by-count":11,"title":["A Structure-Aware Hierarchical Graph-Based Multiple Instance Learning Framework for pT Staging in Histopathological Image"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0180-3086","authenticated-orcid":false,"given":"Jiangbo","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China"}]},{"given":"Lufei","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China"}]},{"given":"Xianli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tencent, Shenzhen, Guangdong, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2365-8318","authenticated-orcid":false,"given":"Zeyu","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2195-2847","authenticated-orcid":false,"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tencent Jarvis Laboratory, Shenzhen, Guangdong, China"}]},{"given":"Chunbao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pathology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3840-441X","authenticated-orcid":false,"given":"Tieliang","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0079-3106","authenticated-orcid":false,"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, China"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_34"},{"key":"ref12","first-page":"2136","article-title":"TransMIL: Transformer based correlated multiple instance learning for whole slide image classification","volume":"34","author":"shao","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1142\/9789811232701_0027"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3216293"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01824"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01567"},{"key":"ref17","first-page":"2127","article-title":"Attention-based deep multiple instance learning","author":"ilse","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.009"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3176598"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-020-00682-w"},{"key":"ref51","first-page":"20689","article-title":"Additive MIL: Intrinsically interpretable multiple instance learning for pathology","author":"javed","year":"2022","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.453"},{"key":"ref45","article-title":"Fast graph representation learning with PyTorch geometric","author":"fey","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2021.100198"},{"key":"ref47","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"chen","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","author":"greene","year":"2002","journal-title":"AJCC Cancer Staging Handbook TNM Classification of Malignant Tumors"},{"key":"ref44","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref43","first-page":"84","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref49","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3084360"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102473"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3086892"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21388"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-1050-2_1"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102482"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3171418"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.3390\/cancers12082031"},{"key":"ref34","article-title":"Deep convolutional networks on graph-structured data","author":"henaff","year":"2015","journal-title":"arXiv 1506 05163"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"ref36","first-page":"933","article-title":"H2-MIL: Exploring hierarchical representation with heterogeneous multiple instance learning for whole slide image analysis","author":"hou","year":"2022","journal-title":"Proc Assoc Adv Artif Intell"},{"key":"ref31","first-page":"4805","article-title":"Hierarchical graph representation learning with differentiable pooling","author":"ying","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref33","first-page":"3734","article-title":"Self-attention graph pooling","author":"lee","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref32","first-page":"2083","article-title":"Graph U-Nets","author":"gao","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref2","first-page":"211","article-title":"Gastric cancer: Overview","volume":"42","author":"correa","year":"2013","journal-title":"Journal of Clinical Gastroenterology"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejso.2020.06.006"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.1883"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4613-0469-2_13"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00050"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102264"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00138"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_33"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01409"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_20"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_59"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_12"},{"key":"ref29","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc Int Conf Learn Represent"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10269089\/10119190.pdf?arnumber=10119190","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,23]],"date-time":"2023-10-23T18:10:36Z","timestamp":1698084636000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10119190\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":51,"journal-issue":{"issue":"10"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2023.3273236","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]}}}