TY  - GEN
N2  - 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.
DO  - 10.6083/dn39x208j
DO  - DOI
AB  - 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.
AD  - Oregon Health and Science University
T1  - Computer-aided diagnosis of prostate cancer using multi-parametric MRI - evaluation of feature extraction and classification
DA  - 2019
AU  - Babcock, Sean
L1  - https://digitalcollections.ohsu.edu/record/7485/files/babcock.sean.2019.pdf
PB  - Oregon Health and Science University
PY  - 2019
ID  - 7485
L4  - https://digitalcollections.ohsu.edu/record/7485/files/babcock.sean.2019.pdf
KW  - Biopsy
KW  - Machine Learning
KW  - Prostatic Neoplasms
KW  - Magnetic Resonance Imaging
KW  - Radiologists
KW  - Image Processing, Computer-Assisted
KW  - Multiparametric Magnetic Resonance Imaging
TI  - Computer-aided diagnosis of prostate cancer using multi-parametric MRI - evaluation of feature extraction and classification
Y1  - 2019
L2  - https://digitalcollections.ohsu.edu/record/7485/files/babcock.sean.2019.pdf
LK  - https://digitalcollections.ohsu.edu/record/7485/files/babcock.sean.2019.pdf
UR  - https://digitalcollections.ohsu.edu/record/7485/files/babcock.sean.2019.pdf
ER  -