Unrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 63; no. 6; pp. 1777 - 1796 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185423485&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185423485 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2025 vid: 63 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185423485 182385296 10.1007/s11517-025-03302-4 185423485 ppf: 1777 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Unrolled deep learning for breast cancer detection using limited-view photoacoustic tomography data. aug: au: John, Mary Barhumi, Imad affil: https://ror.org/01km6p862 Department of Electrical and Communication Engineering, United Arab Emirates University, Asharej, 15551, Al Ain, Abu Dhabi, United Arab Emirates sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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