Image-based computer-aided prognosis of lung cancer: predicting patient recurrent-free survival via a variational Bayesian mixture modeling framework for cluster analysis of CT histograms.
Yoshiki KawataNoboru NikiHironobu OhmatsuMasahiko KusumotoTakaaki TsuchidaKenji EguchiM. KanekoNoriyuki MoriyamaPublished in: Medical Imaging: Computer-Aided Diagnosis (2012)
Keyphrases
- cluster analysis
- prognostic factors
- lung cancer
- computer aided
- pulmonary nodules
- treatment planning
- cancer patients
- early detection
- clustering method
- unsupervised learning
- factor analysis
- mixture model
- expectation maximization
- k means
- topic modeling
- clustering algorithm
- computer aided diagnosis
- data analysis
- ct images
- prostate cancer
- data mining techniques
- computer vision
- data mining
- feature extraction
- matrix factorization
- medical images
- text mining
- risk factors
- ct scans
- survival analysis
- image analysis
- medical imaging
- breast cancer
- image reconstruction
- em algorithm
- active learning
- information retrieval