Deep learning segmentation of triple-negative breast cancer (TNBC) patient derived tumor xenograft (PDX) and sensitivity of radiomic pipeline to tumor probability boundary

Authors

Kaushik Dutta, Washington University School of Medicine in St. Louis
Sudipta Roy, Washington University School of Medicine in St. Louis
Timothy Daniel Whitehead, Washington University School of Medicine in St. Louis
Jingqin Luo, Washington University School of Medicine in St. Louis
Abhinav Kumar Jha, Washington University School of Medicine in St. Louis
Shunqiang Li, Washington University School of Medicine in St. Louis
James Dennis Quirk, Washington University School of Medicine in St. Louis
Kooresh Isaac Shoghi, Washington University School of Medicine in St. Louis

Journal

Cancers (Basel)

Publication Date

2021

Volume

13

Issue

15

First Page

3795

Document Type

Open Access Publication

DOI

10.3390/cancers13153795

Rights and Permissions

Dutta K, Roy S, Whitehead TD, et al. Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary. Cancers (Basel). 2021;13(15):3795. Published 2021 Jul 28. doi:10.3390/cancers13153795

Recommended Citation

Dutta, Kaushik; Roy, Sudipta; Whitehead, Timothy Daniel; Luo, Jingqin; Jha, Abhinav Kumar; Li, Shunqiang; Quirk, James Dennis; and Shoghi, Kooresh Isaac, "Deep learning segmentation of triple-negative breast cancer (TNBC) patient derived tumor xenograft (PDX) and sensitivity of radiomic pipeline to tumor probability boundary." Cancers (Basel). 13, 15. 3795 (2021).
https://digitalcommons.wustl.edu/open_access_pubs/10687