ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography.
Hooman VaseliAng Nan GuS. Neda Ahmadi AmiriMichael Y. TsangAndrea FungNima KondoriArmin SaadatPurang AbolmaesumiTeresa S. M. TsangPublished in: MICCAI (6) (2023)
Keyphrases
- classification accuracy
- training set
- pattern classification
- classification rules
- feature extraction
- pattern recognition
- preprocessing
- feature space
- decision trees
- feature vectors
- machine learning
- supervised learning
- classification method
- automatic classification
- classification scheme
- benchmark datasets
- support vector machine svm
- training samples
- quantitative evaluation
- text classification
- support vector
- feature selection
- patient specific
- classification systems
- classification models
- classification algorithm
- class labels
- dynamic environments
- image classification
- support vector machine