Models (machine learning)
Last revised by Arlene Campos on 8 May 2024
Citation, DOI, disclosures and article data
Citation:
Murphy A, Campos A, Knipe H, Models (machine learning). Reference article, Radiopaedia.org (Accessed on 11 May 2024) https://doi.org/10.53347/rID-68243
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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:
8 May 2024,
Arlene Campos ◉
Disclosures:
At the time the article was last revised Arlene Campos had no financial relationships to ineligible companies to disclose.
View Arlene Campos's current disclosures
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4 times, by
3 contributors -
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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 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:
clustering models
Related articles: Artificial intelligence
- 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
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