Centering
Citation, DOI, disclosures and article data
Citation:
Moore C, Murphy A, Centering. Reference article, Radiopaedia.org (Accessed on 25 Mar 2025) https://doi.org/10.53347/rID-72306
Permalink:
rID:
72306
Article created:
20 Nov 2019,
Candace Makeda Moore
Disclosures:
At the time the article was created Candace Makeda Moore had no recorded disclosures.
View Candace Makeda Moore's current disclosures
Last revised:
Disclosures:
At the time the article was last revised Andrew Murphy had no recorded disclosures.
View Andrew Murphy's current disclosures
Revisions:
2 times, by
2 contributors -
see full revision history and disclosures
Sections:
Centering is a statistical operation on data. In the context of neural networks for image classification related tasks, it implies intensity normalization across images in training data sets. In the context of neural networks specifically for x-ray based images it therefore implies correction for different exposures in different images which will ultimately give the neural network more accurate classification results.
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
Unable to process the form. Check for errors and try again.