Hybrid adaptive attention deep supervision-guided U-Net for breast lesion segmentation in ultrasound computed tomography images.
| Publicado en: | Medical & Biological Engineering & Computing p. 1 |
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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=185804210&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185804210 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2025 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185804210 10.1007/s11517-025-03377-z 185804210 ppf: 1 formats: fmt: @attributes: type: P tig: atl: Hybrid adaptive attention deep supervision-guided U-Net for breast lesion segmentation in ultrasound computed tomography images. aug: au: Liu, Xu Zhou, Liang Cai, Mengyuan Zheng, Hongmei Zeng, Shue Wang, Xiang Wang, Yi Ding, Mingyue affil: Department of Bio-Medical Engineering, School of Life Science and Technology, Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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