000007485 001__ 7485
000007485 005__ 20231228153538.0
000007485 0247_ $$2DOI$$a10.6083/dn39x208j
000007485 037__ $$aETD
000007485 245__ $$aComputer-aided diagnosis of prostate cancer using multi-parametric MRI - evaluation of feature extraction and classification
000007485 260__ $$bOregon Health and Science University
000007485 269__ $$a2019
000007485 336__ $$aDissertation
000007485 502__ $$bM.S.
000007485 520__ $$aThe goal of this project was to evaluate the feasibility of machine learning methods based on feature extraction and classification for differentiating benign and malignant prostate cancer tumors in mp-MRI with the intent of building an algorithm that best reduces over-biopsy and enables in-silico biopsy. The specific aim of this project was to evaluate various extracted texture features, dimensionality reduction, and machine learning classification methods to determine the computer-aided analysis model that provides the greatest classification accuracy for malignant and benign prostate cancer images.
000007485 542__ $$fIn copyright - single owner
000007485 650__ $$aBiopsy$$015582
000007485 650__ $$aMachine Learning$$011449
000007485 650__ $$aProstatic Neoplasms$$024731
000007485 650__ $$aMagnetic Resonance Imaging$$021747
000007485 650__ $$aRadiologists$$011939
000007485 650__ $$aImage Processing, Computer-Assisted$$020650
000007485 650__ $$aMultiparametric Magnetic Resonance Imaging$$013260
000007485 691__ $$aSchool of Medicine$$041369
000007485 692__ $$aDepartment of Medical Informatics and Clinical Epidemiology$$041422
000007485 7001_ $$aBabcock, Sean$$uOregon Health and Science University$$041354
000007485 8564_ $$9a8844cd2-a32b-4346-84f3-77cab89b686f$$s3440398$$uhttps://digitalcollections.ohsu.edu/record/7485/files/babcock.sean.2019.pdf
000007485 905__ $$a/rest/prod/dn/39/x2/08/dn39x208j
000007485 909CO $$ooai:digitalcollections.ohsu.edu:7485$$pstudent-work
000007485 980__ $$aTheses and Dissertations