Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study.
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| Title: | Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study. |
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| Authors: | Jiang M; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan, 430030, Hubei Province, China., Zhang D; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Nantong, China., Tang SC; Department of Medical Ultrasound, Hunan Cancer Hospital/The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan, China., Luo XM; Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China., Chuan ZR; Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118, China., Lv WZ; Department of Artificial Intelligence, Julei Technology Company, Wuhan, 430030, China., Jiang F; Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei, China., Ni XJ; Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Nantong, China., Cui XW; Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan, 430030, Hubei Province, China. cuixinwu@live.cn., Dietrich CF; Department of Internal Medicine, Hirslanden Clinic, Schänzlihalde 11, 3013, Bern, Switzerland. |
| Source: | European radiology [Eur Radiol] 2021 Jun; Vol. 31 (6), pp. 3673-3682. Date of Electronic Publication: 2020 Nov 23. |
| Publication Type: | Journal Article; Multicenter Study |
| Journal Info: | Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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