Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2, 397 breast lesions.
Heather M. WhitneyYu JiHui LiAlexandra EdwardsJohn PapaioannouPeifang LiuMaryellen L. GigerPublished in: Medical Imaging: Computer-Aided Diagnosis (2019)
Keyphrases
- machine learning
- pattern recognition
- machine learning methods
- classification systems
- computer aided detection
- support vector machine
- digital mammograms
- decision trees
- supervised learning
- machine learning approaches
- classification accuracy
- learning systems
- feature selection
- text classification
- machine learning algorithms
- breast tissue
- supervised machine learning
- training set
- model selection
- computer aided
- supervised classification
- image analysis
- breast cancer diagnosis
- computer aided diagnosis
- active learning
- cad systems
- benign and malignant