{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:45:02Z","timestamp":1772826302218,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643681849","type":"print"},{"value":"9781643681856","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,27]]},"abstract":"<jats:p>Medical imaging offers great potential for COVID-19 diagnosis and monitoring. Our work introduces an automated pipeline to segment areas of COVID-19 infection in CT scans using deep convolutional neural networks. Furthermore, we evaluate the performance impact of ensemble learning techniques (Bagging and Augmenting). Our models showed highly accurate segmentation results, in which Bagging achieved the highest dice similarity coefficient.<\/jats:p>","DOI":"10.3233\/shti210223","type":"book-chapter","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T13:23:23Z","timestamp":1622121803000},"source":"Crossref","is-referenced-by-count":3,"title":["COVID-19 Image Segmentation Based on Deep Learning and Ensemble Learning"],"prefix":"10.3233","author":[{"given":"Philip","family":"Meyer","sequence":"first","affiliation":[{"name":"IT-Infrastructure for Translational Medical Research, University of Augsburg"}]},{"given":"Dominik","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"IT-Infrastructure for Translational Medical Research, University of Augsburg"}]},{"given":"I\u00f1aki","family":"Soto-Rey","sequence":"additional","affiliation":[{"name":"IT-Infrastructure for Translational Medical Research, University of Augsburg"}]},{"given":"Frank","family":"Kramer","sequence":"additional","affiliation":[{"name":"IT-Infrastructure for Translational Medical Research, University of Augsburg"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Public Health and Informatics"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210223","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:13:23Z","timestamp":1635167603000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,27]]},"ISBN":["9781643681849","9781643681856"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210223","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,27]]}}}