Models (machine learning)
Last revised by Andrew Murphy on 11 Jun 2019
Citation, DOI, disclosures and article data
Citation:
Murphy A, Knipe H, Models (machine learning). Reference article, Radiopaedia.org (Accessed on 19 Apr 2024) https://doi.org/10.53347/rID-68243
rID:
68243
Article created:
20 May 2019,
Andrew Murphy ◉
Disclosures:
At the time the article was created Andrew Murphy had no recorded disclosures.
View Andrew Murphy's current disclosures
Last revised:
11 Jun 2019,
Andrew Murphy ◉
Disclosures:
At the time the article was last revised Andrew Murphy had no recorded disclosures.
View Andrew Murphy's current disclosures
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3 times, by
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Each machine learning model will vary whilst being determined in part by the type of problem being solved. Although much of the recent work in the field of image processing generally, and more specifically radiology, has focussed on convolutional neural networks, a type of neural network, a number of other models are useful in various circumstances. These include:
- linear regression
- logistic regression
- decision tree
- random forest
- support vector machines
- clustering models
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- artificial intelligence (AI)
- imaging data sets
- computer-aided diagnosis (CAD)
- natural language processing
- machine learning (overview)
- visualizing and understanding neural networks
- common data preparation/preprocessing steps
- DICOM to bitmap conversion
- dimensionality reduction
- scaling
- centering
- normalization
- principal component analysis
- training, testing and validation datasets
- augmentation
- loss function
-
optimization algorithms
- ADAM
- momentum (Nesterov)
- stochastic gradient descent
- mini-batch gradient descent
-
regularisation
- linear and quadratic
- batch normalization
- ensembling
- rule-based expert systems
- glossary
- activation function
- anomaly detection
- automation bias
- backpropagation
- batch size
- computer vision
- concept drift
- cost function
- confusion matrix
- convolution
- cross validation
- curse of dimensionality
- dice similarity coefficient
- dimensionality reduction
- epoch
- explainable artificial intelligence/XAI
- feature extraction
- federated learning
- gradient descent
- ground truth
- hyperparameters
- image dataset normalization
- image registration
- imputation
- iteration
- jaccard index
- linear algebra
- noise reduction
- normalization
- R (Programming language)
- radiomics quality score (RQS)
- Python (Programming language)
- segmentation
- semi-supervised learning
- synthetic and augmented data
- overfitting
- underfitting
- transfer learning