{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T04:22:43Z","timestamp":1771647763387,"version":"3.50.1"},"reference-count":18,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T00:00:00Z","timestamp":1569888000000},"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":[[2019,10]]},"DOI":"10.1109\/cisp-bmei48845.2019.8965660","type":"proceedings-article","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T22:24:14Z","timestamp":1579818254000},"page":"1-5","source":"Crossref","is-referenced-by-count":15,"title":["PNet: An Efficient Network for Pneumonia Detection"],"prefix":"10.1109","author":[{"given":"Zhongliang","family":"Li","sequence":"first","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University,Shenzhen,P.R. China"}]},{"given":"Juan","family":"Yu","sequence":"additional","affiliation":[{"name":"Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University,Shenzhen,China"}]},{"given":"Xuechen","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University,Shenzhen,P.R. China"}]},{"given":"Yingqi","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen Bao'an District Songgang People's Hospital,Imaging Department,Shenzhen,China"}]},{"given":"Weicai","family":"Dai","sequence":"additional","affiliation":[{"name":"Fifth People's Hospital of Longgang District,Imaging Department,Shenzhen,China"}]},{"given":"Linlin","family":"Shen","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University,Shenzhen,P.R. China"}]},{"given":"Lisha","family":"Mou","sequence":"additional","affiliation":[{"name":"Shenzhen University Health Science Center, Shenzhen University, School of Medicine,Imaging Department,Shenzhen,China"}]},{"given":"Zuhui","family":"Pu","sequence":"additional","affiliation":[{"name":"Shenzhen Second People's Hospital, First Affiliated Hospital of Shenzhen University,Shenzhen,China"}]}],"member":"263","reference":[{"key":"ref10","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref11","author":"simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref14","author":"yao","year":"2017","journal-title":"Learning to diagnose from scratch by exploiting dependencies among labels"},{"key":"ref15","author":"pesce","year":"2017","journal-title":"Learning to detect chest radiographs containing lung nodules using visual attention networks"},{"key":"ref16","author":"rajpurkar","year":"2017","journal-title":"CheXNet Radiologist-level pneumonia detection on chest X-rays with deep learning"},{"key":"ref17","author":"guan","year":"2018","journal-title":"Diagnose like a radiologist Attention guided convolutional neural network for thorax disease classification"},{"key":"ref18","author":"zeiler","year":"2012","journal-title":"ADADELTA An Adaptive Learning Rate Method"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.07.007"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2008.2004421"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2788044"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2009.5193056"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.02.015"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-016-0436-2"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2261011924"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.182.1.1727272"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.3390\/s19092167","article-title":"Image Thresholding Improves 3-Dimensional Convolutional Neural Network Diagnosis of Different Acute Brain Hemorrhages on Computed Tomography Scans","volume":"19","author":"ker","year":"2019","journal-title":"SENSORS"}],"event":{"name":"2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","location":"Suzhou, China","start":{"date-parts":[[2019,10,19]]},"end":{"date-parts":[[2019,10,21]]}},"container-title":["2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8955993\/8965652\/08965660.pdf?arnumber=8965660","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T19:22:06Z","timestamp":1756754526000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8965660\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1109\/cisp-bmei48845.2019.8965660","relation":{},"subject":[],"published":{"date-parts":[[2019,10]]}}}