Using optimal transport theory to optimize a deep convolutional neural network microscopic cell counting method.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 61; no. 11; pp. 2939 - 2951 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2023
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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=173036042&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173036042 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2023 vid: 61 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173036042 10.1007/s11517-023-02862-7 173036042 ppf: 2939 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using optimal transport theory to optimize a deep convolutional neural network microscopic cell counting method. aug: au: Ding, Yuanyuan Zheng, Yuanjie Han, Zeyu Yang, Xinbo affil: https://ror.org/01wy3h363 School of Information Science and Engineering, Shandong Normal University, 250358, Jinan, Shandong, China sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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