Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI.

MLA

Lin, Yu-Chun, et al. “Generalizable Transfer Learning of Automated Tumor Segmentation from Cervical Cancers toward a Universal Model for Uterine Malignancies in Diffusion-Weighted MRI.” Insights into Imaging, vol. 14, no. 1, Jan. 2023, p. 14. EBSCOhost, https://doi.org/10.1186/s13244-022-01356-8.

APA

Lin, Y.-C., Lin, Y., Huang, Y.-L., Ho, C.-Y., Chiang, H.-J., Lu, H.-Y., Wang, C.-C., Wang, J.-J., Ng, S.-H., Lai, C.-H., & Lin, G. (2023). Generalizable transfer learning of automated tumor segmentation from cervical cancers toward a universal model for uterine malignancies in diffusion-weighted MRI. Insights into Imaging, 14(1), 14. https://doi.org/10.1186/s13244-022-01356-8

Chicago

Lin, Yu-Chun, Yenpo Lin, Yen-Ling Huang, Chih-Yi Ho, Hsin-Ju Chiang, Hsin-Ying Lu, Chun-Chieh Wang, et al. 2023. “Generalizable Transfer Learning of Automated Tumor Segmentation from Cervical Cancers toward a Universal Model for Uterine Malignancies in Diffusion-Weighted MRI.” Insights into Imaging 14 (1): 14. doi:10.1186/s13244-022-01356-8.