Improving prostate cancer triage with GAN-based synthetically generated prostate ADC MRI
In person: 23 February 2022 • 5:30 PM - 7:00 PM PST
Prostate Cancer (PCa) is the third most commonly diagnosed cancer worldwide. In spite of it, its diagnostic is hampered by a substantial overdiagnosis. Magnetic Resonance Imaging (MRI) has proven to be reliable to differentiate between clinically significant (cS) and non-clinically significant (nCs) cases but it requires specialized training. Deep learning (DL) has arisen as an alternative to automatize manual MRI analysis but they require large amounts of annotated data. Standard augmentation techniques such as image translation have become the default option to increase data availability. However, the correlation between transformed data and the original one limits the amount of information provided by such a method. Generative Adversarial Networks (GAN) present an alternative. We explore a cGAN and DCGAN architecture to generate ADC MRI prostate samples and show how their addition improves final results measured by area under the curve (AUC) on a prostate cancer triage application.
Univ. of Stavanger (Norway)
Alvaro Fernandez-Quilez was born in Barcelona, Spain. He obtained his degree in Telecommunications engineering and computer science at the Polytechnic University of Catalonia. Immediately after finishing his degree, he obtained his master's degree in Computational Biomedical engineering at University Pompeu Fabra (UPF). He is currently living in Norway, where he is pursuing a PhD at University of Stavanger, focusing on artificial intelligence applied to prostate cancer.