Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis-Reference-Cited by-同舟云学术

Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis

Published:2023-07-19 Issue:3 Volume:16 Page:406-413
ISSN:1865-0333
Container-title:Radiological Physics and Technology
language:en
Short-container-title:Radiol Phys Technol

Author:

Shimokawa Daiki,Takahashi Kengo,Oba Ken,Takaya Eichi,Usuzaki Takuma,Kadowaki Mizuki,Kawaguchi Kurara,Adachi Maki,Kaneno Tomofumi,Fukuda Toshinori,Yagishita Kazuyo,Tsunoda Hiroko,Ueda Takuya^ORCID

Funder

Core Research for Evolutional Science and Technology

MEXT/JSPS WISE Program: Advanced Graduate Program for Future Medicine and Health Care

Publisher

Springer Science and Business Media LLC

Subject

Radiology, Nuclear Medicine and imaging,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine,Radiation

Link

https://link.springer.com/content/pdf/10.1007/s12194-023-00731-4.pdf

Reference27 articles.

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3. Fisher ER, Gregorio RM, Fisher B, et al. The pathology of invasive breast cancer: a syllabus derived from findings of the national surgical adjuvant breast project (Protocol No. 4). Cancer. 1975;36:1–85.

4. Harbeck N, Gnant M. Breast cancer. Lancet. 2017;389:1134–50.

5. Tafreshi NK, Kumar V, Morse DL, et al. Molecular and functional imaging of breast cancer. Cancer Control. 2010;17:143–55.

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