A U-Net applied to diffusion-weighted imaging (DWI) only was trained with 3T MRI data from a single system in 316 consecutive patients. All clinical MR lesions were targeted with fusion biopsy in addition to extended 24-core systematic biopsy. The performance of the final CNN ensemble on the test set achieved comparable sensitivity in comparison to multiparametric clinical assessment and demonstrated the method’s ability to generate stable results in an unseen subset. These findings highlight the ability of computer vision to closely model the clinical task with fewer data and encourage development of the method in larger cohorts.
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