{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T04:18:26Z","timestamp":1773980306312,"version":"3.50.1"},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"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":[[2021,4,13]]},"DOI":"10.1109\/isbi48211.2021.9433876","type":"proceedings-article","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T20:14:15Z","timestamp":1621973655000},"page":"1077-1081","source":"Crossref","is-referenced-by-count":17,"title":["Federated Learning for Site Aware Chest Radiograph Screening"],"prefix":"10.1109","author":[{"given":"Arunava","family":"Chakravarty","sequence":"first","affiliation":[]},{"given":"Avik","family":"Kar","sequence":"additional","affiliation":[]},{"given":"Ramanathan","family":"Sethuraman","sequence":"additional","affiliation":[]},{"given":"Debdoot","family":"Sheet","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Learning to diagnose from scratch by exploiting dependencies among labels","author":"yao","year":"2017","journal-title":"arXiv preprint arXiv 1710 10501"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9176693"},{"key":"ref12","first-page":"181","article-title":"Federated learning for breast density classification: A real-world implementation","author":"roth","year":"2020","journal-title":"Domain Adaptation and Representation Transfer and Distributed and Collaborative Learning"},{"key":"ref13","first-page":"1","article-title":"Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"sheller","year":"2020","journal-title":"Nature Scientific Reports"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref15","first-page":"29","article-title":"Dynamic edge-conditioned filters in convolutional neural networks on graphs","author":"martin","year":"2017","journal-title":"IEEE CVPR"},{"key":"ref16","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref17","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"ICLRE"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref3","first-page":"770","article-title":"Deep residual learning for image recognition","author":"kaiming","year":"2016","journal-title":"IEEE CVPR"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2916849"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"ref8","article-title":"Can artificial intelligence reliably report chest x-rays?: Radiologist validation of an algorithm trained on 2.3 million x-rays","author":"putha","year":"2018","journal-title":"arXiv 1807 07455"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"6381","DOI":"10.1038\/s41598-019-42294-8","article-title":"Comparison of deep learning approaches for multi-label chest x-ray classification","volume":"9","author":"baltruschat","year":"2019","journal-title":"Nature Scientific Reports"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9175246"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"4683j","DOI":"10.1136\/bmj.j4683","article-title":"Radiologist shortage leaves patient care at risk, warns royal college","volume":"359","author":"rimmer","year":"2017","journal-title":"BMJ"},{"key":"ref9","article-title":"Interpreting chest x-rays via cnns that exploit hierarchical disease dependencies and uncertainty labels","author":"pham","year":"2020","journal-title":"MIDL"}],"event":{"name":"2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)","location":"Nice, France","start":{"date-parts":[[2021,4,13]]},"end":{"date-parts":[[2021,4,16]]}},"container-title":["2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9433749\/9433753\/09433876.pdf?arnumber=9433876","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:41:46Z","timestamp":1652197306000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9433876\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,13]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1109\/isbi48211.2021.9433876","relation":{},"subject":[],"published":{"date-parts":[[2021,4,13]]}}}