SpaceTime-SonoNet: efficient classification of ultra-sound video sequences.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 64; no. 3; pp. 1167 - 1179 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Mar2026
|
| 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=192845038&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192845038 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2026 vid: 64 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 192845038 191201475 10.1007/s11517-025-03504-w 192845038 ppf: 1167 ppct: 12 formats: tig: atl: SpaceTime-SonoNet: efficient classification of ultra-sound video sequences. aug: au: Interlando, Matteo Zini, Luca Guraschi, Nicola Noceti, Nicoletta Odone, Francesca affil: https://ror.org/0107c5v14 MaLGa-DIBRIS, Università degli Studi di Genova, Genova, Italy sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
|---|