Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer

MLA

Ying Hou, et al. “Integration of Clinicopathologic Identification and Deep Transferrable Image Feature Representation Improves Predictions of Lymph Node Metastasis in Prostate Cancer.” EBioMedicine, vol. 68, no. 103395-, June 2021. EBSCOhost, https://doi.org/10.1016/j.ebiom.2021.103395.

APA

Ying Hou, Jie Bao, Yang Song, Mei-Ling Bao, Ke-Wen Jiang, Jing Zhang, Guang Yang, Chun-Hong Hu, Hai-Bin Shi, Xi-Ming Wang, & Yu-Dong Zhang. (2021). Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer. EBioMedicine, 68(103395-). https://doi.org/10.1016/j.ebiom.2021.103395

Chicago

Ying Hou, Jie Bao, Yang Song, Mei-Ling Bao, Ke-Wen Jiang, Jing Zhang, Guang Yang, et al. 2021. “Integration of Clinicopathologic Identification and Deep Transferrable Image Feature Representation Improves Predictions of Lymph Node Metastasis in Prostate Cancer.” EBioMedicine 68 (103395-). doi:10.1016/j.ebiom.2021.103395.