Computational learning methods for early detection of ovarian cancer

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

doi:10.1117/12.2594225

1 August 2021 Computational learning methods for early detection of ovarian cancer

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

Author Affiliations +

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041A (2021) https://doi.org/10.1117/12.2594225
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

Artificial Intelligence methods can be very effective in classification tasks that involve the processing of ordered sequences of data. Here we explore two different approaches to tackle the problem of ovarian cancer detection from a sequence of longitudinal measurements of several biomarkers. The first approach relies on a Bayesian hierarchical model whose fundamental assumption is that measurements taken from case subjects exhibit a changepoint in one or several biomarkers. The second approach is a purely discriminative machine learning algorithm based on the use of recurrent neural networks, a kind of artificial neural network specially suited to the processing of inputs of different lengths.

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Ines P. Mariño, Manuel A. Vázquez, Oleg Blyuss, Andy Ryan, Aleksandra Gentry-Maharaj, Jatinderpal Kalsi, Ranjit Manchanda, Ian Jacobs, Usha Menon, and Alexey Zaikin "Computational learning methods for early detection of ovarian cancer", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041A (1 August 2021); https://doi.org/10.1117/12.2594225

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