A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer

Manuel A. Vázquez, Inés P. Mariño, Oleg Blyuss, Andy Ryan, Aleksandra Gentry-Maharaj, Jatinderpal Kalsi, Ranjit Manchanda, Ian J. Jacobs, Usha Menon, Alexey Zaikin

Abstract: Highlights•We tackle the problem of early detection of ovariance cancer using longitudinal measurements of multiple biomarkers.•We compare two different paradigms: Bayesian methods and deep learning.•We provide evidence that using multiple biomarkers yields a performance boost as compared to the standard screening test using CA125 alone.