Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification

MLA

Philps, Ben, et al. Uncertainty Quantification for White Matter Hyperintensity Segmentation Detects Silent Failures and Improves Automated Fazekas Quantification. 2024. EBSCOhost, widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2411.17571&authtype=sso&custid=ns315887.

APA

Philps, B., Hernandez, M. del C. V., Qin, C., Clancy, U., Sakka, E., Maniega, S. M., Bastin, M. E., Jochems, A. C. C., Wardlaw, J. M., Bernabeu, M. O., & Initiative, A. D. N. (2024). Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification.

Chicago

Philps, Ben, Maria del C. Valdes Hernandez, Chen Qin, Una Clancy, Eleni Sakka, Susana Munoz Maniega, Mark E. Bastin, et al. 2024. “Uncertainty Quantification for White Matter Hyperintensity Segmentation Detects Silent Failures and Improves Automated Fazekas Quantification.” http://widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2411.17571&authtype=sso&custid=ns315887.