{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T17:02:34Z","timestamp":1771952554507,"version":"3.50.1"},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,19]]},"DOI":"10.1109\/icip42928.2021.9506553","type":"proceedings-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T21:08:41Z","timestamp":1629752921000},"page":"205-209","source":"Crossref","is-referenced-by-count":3,"title":["Exploiting Deep Cross-Slice Features From CT Images For Multi-Class Pneumonia Classification"],"prefix":"10.1109","author":[{"given":"Jiawang","family":"Cao","sequence":"first","affiliation":[{"name":"Fudan University,Academy of Engineering & Technology,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lulu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fudan University,Academy of Engineering & Technology,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junlin","family":"Hou","sequence":"additional","affiliation":[{"name":"Fudan University,Academy of Engineering & Technology,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longquan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fudan University,Academy of Engineering & Technology,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Fudan University,Academy of Engineering & Technology,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiya","family":"Shi","sequence":"additional","affiliation":[{"name":"Fudan University,Academy of Engineering & Technology,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Shan","sequence":"additional","affiliation":[{"name":"Fudan University,Academy of Engineering & Technology,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Feng","sequence":"additional","affiliation":[{"name":"Fudan University,Academy of Engineering & Technology,Shanghai,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","article-title":"Deep learning-based detection for covid-19 from chest ct using weak label","author":"zheng","year":"2020","journal-title":"medRxiv"},{"key":"ref11","article-title":"Med3d: Transfer learning for 3d medical image analysis","volume":"abs 1904 625","author":"chen","year":"2019","journal-title":"CoRR"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2995965"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3030853"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2599174"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2892409"},{"key":"ref16","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","volume":"97","author":"tan","year":"2019","journal-title":"Proceedings of the 36th International Conference on Machine Learning"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2995508"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-06928-0"},{"key":"ref6","first-page":"200082e","article-title":"Longitudinal assessment of covid-19 using a deep learning–based quantitative ct pipeline: Illustration of two cases","volume":"2","author":"cao","year":"2020","journal-title":"Journal of Thoracic Imaging"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.21037\/atm.2020.03.132"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"200075e","DOI":"10.1148\/ryct.2020200075","article-title":"Serial quantitative chest ct assessment of covid19: Deep-learning approach","volume":"2","author":"huang","year":"2020","journal-title":"Radiol Cardiothoracic Imag"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200230"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30086-4"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020200905"}],"event":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","location":"Anchorage, AK, USA","start":{"date-parts":[[2021,9,19]]},"end":{"date-parts":[[2021,9,22]]}},"container-title":["2021 IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9506008\/9506009\/09506553.pdf?arnumber=9506553","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:02:05Z","timestamp":1670371325000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9506553\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,19]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1109\/icip42928.2021.9506553","relation":{},"subject":[],"published":{"date-parts":[[2021,9,19]]}}}