Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis.
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| Title: | Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis. |
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| Authors: | Li, Zhongliang1, Li, Xuechen2, Jin, Zhihao1, Shen, Linlin1, llshen@email.szu.edu.cn |
| Source: | Neural Computing & Applications; May2023, Vol. 35 Issue 15, p10717-10731, 15p |
| Database: | Applied Science & Technology Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: aci DbLabel: Applied Science & Technology Source An: 163294462 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-023-08259-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 10717 Titles: – TitleFull: Learning from pseudo-lesion: a self-supervised framework for COVID-19 diagnosis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Zhongliang – PersonEntity: Name: NameFull: Li, Xuechen – PersonEntity: Name: NameFull: Jin, Zhihao – PersonEntity: Name: NameFull: Shen, Linlin IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 05 Text: May2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 35 – Type: issue Value: 15 Titles: – TitleFull: Neural Computing & Applications Type: main |
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