Prostate Cancer Localization With Multispectral MRI Using Cost-Sensitive Support Vector Machines and Conditional Random Fields.
Yusuf ArtanMasoom A. HaiderDeanna L. LangerTheodorus H. van der KwastAndrew J. EvansYongyi YangMiles N. WernickJohn TrachtenbergImam Samil YetikPublished in: IEEE Trans. Image Process. (2010)
Keyphrases
- multispectral
- cost sensitive
- conditional random fields
- prostate cancer
- binary classification
- support vector
- mr images
- image data
- support vector machine
- multi class
- medical image analysis
- graphical models
- hidden markov models
- remote sensing
- image analysis
- medical images
- naive bayes
- probabilistic model
- class distribution
- active learning
- pairwise
- markov random field
- logistic regression
- multi class classification
- higher order
- information extraction
- generative model
- class imbalance
- classification accuracy
- medical imaging
- loss function
- training examples
- feature selection
- binary classifiers
- kernel function
- hyperplane
- svm classifier
- multiclass classification
- segmentation method
- superpixels
- maximum margin
- base classifiers
- training set
- bayesian networks
- training data
- data mining