Dutta, K.; Roy, S.; Whitehead, T.D.; Luo, J.; Jha, A.K.; Li, S.; Quirk, J.D.; Shoghi, K.I.
Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary. Cancers 2021, 13, 3795.
https://doi.org/10.3390/cancers13153795
AMA Style
Dutta K, Roy S, Whitehead TD, Luo J, Jha AK, Li S, Quirk JD, Shoghi KI.
Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary. Cancers. 2021; 13(15):3795.
https://doi.org/10.3390/cancers13153795
Chicago/Turabian Style
Dutta, Kaushik, Sudipta Roy, Timothy Daniel Whitehead, Jingqin Luo, Abhinav Kumar Jha, Shunqiang Li, James Dennis Quirk, and Kooresh Isaac Shoghi.
2021. "Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary" Cancers 13, no. 15: 3795.
https://doi.org/10.3390/cancers13153795
APA Style
Dutta, K., Roy, S., Whitehead, T. D., Luo, J., Jha, A. K., Li, S., Quirk, J. D., & Shoghi, K. I.
(2021). Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary. Cancers, 13(15), 3795.
https://doi.org/10.3390/cancers13153795
