{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T02:25:17Z","timestamp":1725589517117},"reference-count":6,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,4]]},"DOI":"10.1117\/12.2612700","type":"proceedings-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T15:04:57Z","timestamp":1648825497000},"page":"116","source":"Crossref","is-referenced-by-count":1,"title":["Co-occurring diseases heavily influence the performance of weakly supervised learning models for classification of chest CT"],"prefix":"10.1117","author":[{"given":"Fakrul Islam","family":"Tushar","sequence":"first","affiliation":[]},{"given":"Vincent M.","family":"D'Anniballe","sequence":"additional","affiliation":[]},{"given":"Geoffrey D.","family":"Rubin","sequence":"additional","affiliation":[]},{"given":"Ehsan","family":"Samei","sequence":"additional","affiliation":[]},{"given":"Joseph Y.","family":"Lo","sequence":"additional","affiliation":[]}],"member":"189","reference":[{"key":"c1","article-title":"Padchest: A large chest x-ray image dataset with multi-label annotated reports","author":"Bustos","year":"2019","journal-title":"arXiv preprint arXiv:1901.07441"},{"key":"c2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0322-0"},{"key":"c3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101857"},{"key":"c4","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.369","article-title":"ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases","author":"Wang","year":"2017"},{"key":"c5","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2100261"},{"key":"c6","article-title":"Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning","author":"D'Anniballe","year":"2021","journal-title":"arXiv preprint arXiv:2102.02959"}],"event":{"name":"Computer-Aided Diagnosis","start":{"date-parts":[[2022,2,20]]},"location":"San Diego, United States","end":{"date-parts":[[2022,3,28]]}},"container-title":["Medical Imaging 2022: Computer-Aided Diagnosis"],"original-title":[],"deposited":{"date-parts":[[2022,7,2]],"date-time":"2022-07-02T21:30:46Z","timestamp":1656797446000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12033\/2612700\/Co-occurring-diseases-heavily-influence-the-performance-of-weakly-supervised\/10.1117\/12.2612700.full"}},"subtitle":[],"editor":[{"given":"Khan M.","family":"Iftekharuddin","sequence":"additional","affiliation":[]},{"given":"Karen","family":"Drukker","sequence":"additional","affiliation":[]},{"given":"Maciej A.","family":"Mazurowski","sequence":"additional","affiliation":[]},{"given":"Hongbing","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Chisako","family":"Muramatsu","sequence":"additional","affiliation":[]},{"given":"Ravi K.","family":"Samala","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,4,4]]},"references-count":6,"URL":"https:\/\/doi.org\/10.1117\/12.2612700","relation":{},"subject":[],"published":{"date-parts":[[2022,4,4]]}}}