Nielsen, P.S.; Georgsen, J.B.; Vinding, M.S.; Østergaard, L.R.; Steiniche, T.
Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma. Int. J. Environ. Res. Public Health 2022, 19, 14327.
https://doi.org/10.3390/ijerph192114327
AMA Style
Nielsen PS, Georgsen JB, Vinding MS, Østergaard LR, Steiniche T.
Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma. International Journal of Environmental Research and Public Health. 2022; 19(21):14327.
https://doi.org/10.3390/ijerph192114327
Chicago/Turabian Style
Nielsen, Patricia Switten, Jeanette Baehr Georgsen, Mads Sloth Vinding, Lasse Riis Østergaard, and Torben Steiniche.
2022. "Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma" International Journal of Environmental Research and Public Health 19, no. 21: 14327.
https://doi.org/10.3390/ijerph192114327
APA Style
Nielsen, P. S., Georgsen, J. B., Vinding, M. S., Østergaard, L. R., & Steiniche, T.
(2022). Computer-Assisted Annotation of Digital H&E/SOX10 Dual Stains Generates High-Performing Convolutional Neural Network for Calculating Tumor Burden in H&E-Stained Cutaneous Melanoma. International Journal of Environmental Research and Public Health, 19(21), 14327.
https://doi.org/10.3390/ijerph192114327