Unsupervised Machine Learning Exploration of Morphological and Haemodynamic Indices to Predict Thrombus Formation in the Left Atrial Appendage.
Marta Saiz-VivóJordi MillJosquin HarrisonGuillermo Jiménez-PérezBenoit LeggheXavier IriartHubert CochetGemma PiellaMaxime SermesantOscar CamaraPublished in: STACOM@MICCAI (2022)
Keyphrases
- machine learning
- supervised learning
- unsupervised learning
- supervised classification
- data mining
- statistical methods
- explanation based learning
- mathematical morphology
- semi supervised learning
- data driven
- computational intelligence
- image processing
- learning algorithm
- computer science
- pattern recognition
- inductive learning
- learning problems
- decision trees
- knowledge acquisition
- semi supervised
- information extraction
- machine learning approaches
- multiscale
- learning systems
- knowledge representation
- coronary artery
- learning tasks
- morphological operators
- machine learning methods
- deep learning
- feature selection
- neural network
- natural language processing
- active learning
- support vector machine