@article{ETD,
      school = {M.S.},
      author = {Babcock, Sean},
      url = {http://digitalcollections.ohsu.edu/record/7485},
      title = {Computer-aided diagnosis of prostate cancer using  multi-parametric MRI - evaluation of feature extraction and  classification},
      publisher = {Oregon Health and Science University},
      abstract = {The 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.},
      number = {ETD},
      doi = {https://doi.org/10.6083/dn39x208j},
      recid = {7485},
      address = {2019},
}