Computer-aided diagnosis of prostate cancer using multi-parametric MRI - Evaluation of feature extraction and classification Public Deposited

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 model used regions of interest (ROI) in 2D multiparametric magnetic resonance images (mp-MRI) selected by the radiologist as possible prostate cancer tumors. 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. From these image level models, a patient level diagnosis decision support model was determined that best diagnoses malignant cancer patients.

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  • 0000-0002-5615-5373
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Identifier
  • 10.6083/dn39x208j
  • babcock.sean.2019.pdf
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Date
  • 2019
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Citation
  • Babcock, S. "Computer-aided diagnosis of prostate cancer using multi-parametric MRI - Evaluation of feature extraction and classification " (2019). OHSU Digital Collections. https://doi.org/10.6083/dn39x208j
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