Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features.
Saved in:
| Title: | Classifying the molecular subtype of breast cancer using vision transformer and convolutional neural network features. |
|---|---|
| Authors: | Kai C; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, 1398 Shimamichou, Kita-Ku, Niigata, Japan.; Major in Health and Welfare, Graduate School of Niigata, University of Health and Welfare, Niigata, Japan., Tamori H; The Asahi Shimbun Company, Tokyo, Japan., Ohtsuka T; Ohtsuka Breastcare Clinic, Tokyo, Japan., Nara M; Ohtsuka Breastcare Clinic, Tokyo, Japan., Yoshida A; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, 1398 Shimamichou, Kita-Ku, Niigata, Japan., Sato I; Major in Health and Welfare, Graduate School of Niigata, University of Health and Welfare, Niigata, Japan.; Department of Nursing, Faculty of Nursing, Niigata University of Health and Welfare, Niigata City, Niigata, Japan., Futamura H; KONICA MINOLTA, INC, Tokyo, Japan., Kodama N; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, 1398 Shimamichou, Kita-Ku, Niigata, Japan., Kasai S; Department of Radiological Technology, Faculty of Medical Technology, Niigata University of Health and Welfare, 1398 Shimamichou, Kita-Ku, Niigata, Japan. satoshi-kasai@nuhw.ac.jp. |
| Source: | Breast cancer research and treatment [Breast Cancer Res Treat] 2025 Apr; Vol. 210 (3), pp. 771-782. Date of Electronic Publication: 2025 Jan 22. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Kluwer Academic Country of Publication: Netherlands NLM ID: 8111104 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-7217 (Electronic) Linking ISSN: 01676806 NLM ISO Abbreviation: Breast Cancer Res Treat Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
|
Full text is not displayed to guests.
Login for full access.
|
|
| ISSN: | 1573-7217 |
|---|---|
| DOI: | 10.1007/s10549-025-07614-9 |