Leng, Ethan2020-09-222020-09-222020-07https://hdl.handle.net/11299/216369University of Minnesota Ph.D. dissertation. July 2020. Major: Biomedical Engineering. Advisor: Gregory Metzger. 1 computer file (PDF); x, 164 pages.Prostate cancer (PCa) is a leading cause of cancer death among men in the U.S. Multiparametric magnetic resonance imaging (mpMRI), a combination of anatomic and functional imaging methods, has demonstrated potential to improve PCa detection. However, radiologic interpretation of mpMRI data is time-consuming and highly dependent on reader expertise. Therefore, a computer-aided detection (CAD) system that could accurately and automatically detect PCa using mpMRI data would provide tremendous clinical utility. This dissertation research focuses on the development and improvement of several components of such a CAD system, with topics that include image registration, quantitative pathology, and model development and evaluation.encomputer-aided diagnosisdigital pathologyimage processingmachine learningprostate cancerThesis or Dissertation