Non-invasive prediction of lymph node risk in oral cavity cancer patients using a combination of supervised and unsupervised machine learning algorithms.
Alberto TraversoAli Hosni-AbdalatyMohammad HasanTony TadicTirth PatelMeredith GiulianiJohn KimJolie RingashJohn ChoScott V. BratmanAndrew BayleyJohn WaldronBrian O'SullivanJonathan C. IrishDouglas ChepehaJohn De AlmeidaDavid GoldsteinDavid A. JaffrayLeonard WeeAndre DekkerAndrew HopePublished in: Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging (2020)
Keyphrases
- machine learning algorithms
- cancer patients
- lymph nodes
- learning algorithm
- machine learning
- automatic detection
- supervised learning
- unsupervised learning
- semi supervised
- clinical data
- decision trees
- automatic segmentation
- machine learning methods
- lung cancer
- clinical trials
- prediction accuracy
- breast cancer
- cancer diagnosis
- ct data
- clinical practice
- prostate cancer
- ct images
- feature selection
- three dimensional
- ct scans
- active learning
- medical data
- risk assessment
- training data
- cross sections
- raw data
- naive bayes
- support vector machine