Deep Learning Segmentation of Triple-Negative Breast Cancer (TNBC) Patient Derived Tumor Xenograft (PDX) and Sensitivity of Radiomic Pipeline to Tumor Probability Boundary.

MLA

Dutta, Kaushik, 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, vol. 13, no. 15, Aug. 2021, p. 3795. EBSCOhost, https://doi.org/10.3390/cancers13153795.

APA

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

Chicago

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 (15): 3795. doi:10.3390/cancers13153795.