dc.contributor.author | Paluru, Naveen | |
dc.contributor.author | Dayal, Aveen | |
dc.contributor.author | Jenssen, Håvard | |
dc.contributor.author | Sakinis, Tomas | |
dc.contributor.author | Cenkeramaddi, Linga Reddy | |
dc.contributor.author | Jaya, Prakash | |
dc.contributor.author | Yalavarthy, Phaneendra K. | |
dc.date.accessioned | 2022-04-22T11:55:51Z | |
dc.date.available | 2022-04-22T11:55:51Z | |
dc.date.created | 2021-06-04T14:53:53Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Paluru, N., Dayal, A., Jenssen, H., Sakinis, T., Cenkeramaddi, L.R., Jaya, P. & Yalavarthy, P.K. (2021) Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images IEEE Transactions on Neural Networks and Learning Systems. 2021, 32 (3), 932-946. | en_US |
dc.identifier.issn | 2162-237X | |
dc.identifier.uri | https://hdl.handle.net/11250/2992293 | |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.title | Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | submittedVersion | en_US |
dc.rights.holder | © 2021 The Author(s) | en_US |
dc.subject.nsi | VDP::Technology: 500 | en_US |
dc.source.pagenumber | 932-946 | en_US |
dc.source.volume | 32 | en_US |
dc.source.journal | IEEE Transactions on Neural Networks and Learning Systems | en_US |
dc.source.issue | 3 | en_US |
dc.identifier.doi | 10.1109/TNNLS.2021.3054746 | |
dc.identifier.cristin | 1913815 | |
dc.relation.project | Norges forskningsråd: 287918 | en_US |
cristin.qualitycode | 2 | |