Anomaly classification in digital mammography based on multiple-instance learning.
Abdelali ElmoufidiKhalid El FahssiSaid Jai-AndaloussiAbderrahim SekkakiGwénolé QuellecMathieu LamardPublished in: IET Image Process. (2018)
Keyphrases
- multiple instance learning
- image categorization
- supervised learning
- digital mammography
- multiple instance
- digital mammograms
- class labels
- instance selection
- classification accuracy
- pattern recognition
- feature vectors
- feature selection
- support vector
- multi class
- text classification
- machine learning
- semi supervised
- image classification
- semi supervised learning
- feature space
- feature extraction
- graph cuts
- unsupervised learning
- training samples
- support vector machine
- image annotation
- learning process
- decision trees