Elisa Tedde
Deep Learning for Assessing Risk of Prostate Cancer.
Rel. Santa Di Cataldo, Massimiliano Ruocco, Francesco Ponzio. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2022
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Abstract
Cancer detection is one of the leading research topics in medical science. Prostate cancer is the second most common cancer in men and the sixth leading cause of cancer death among men in the world. The current diagnostic pathway is based on serum Prostate-Specific Antigen (PSA) levels. Although PSA screening reduces the spread and death from cancer, it overdiagnoses some low-risk cancers that may not have caused damage, leading to unnecessary invasive examinations. Several methods have been proposed in the past, such as studying the evolution of prostate antigen over time using different velocity formulas, which have often led to inconsistent results.
In this thesis, some Deep Learning methods have been applied to time series to predict prostate cancer risk
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