Generative Adversarial Networks (GANs) were evaluated for detection and visualization of prostate cancer, proposing an automated end-to-end pipeline. Two GANs were trained and tested with T2-weighted images from an in-house dataset of 646 patients. The weakly-supervised GAN performed better (AUC=0.785) than unsupervised GAN (AUC=0.462). The performance of the GANs was dependent on pre-processing parameters. The PROSTATEx dataset (N=204) was used for external validation, giving an AUC of 0.642. The weakly-supervised GAN showed promise for detecting and localizing prostate cancer on T2W MRI, but further research is necessary to improve model performance and generalizability.
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