Scaling

Scaling is a linear transformation that changes the size of a mathematical object. The mathematical objects of interest to radiologists that can be scaled are usually image matrices. This simple type of spatial normalization is a common step in image normalization for creating an image data set for AI program creation. Feature scaling is a type of scaling. Scaling can also be used in the creation of augmented data. Scaling need not be isotropic.

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Artificial intelligence

• artificial intelligence (AI)
• imaging data sets
• computer-aided diagnosis (CAD)
• natural language processing
• machine learning (overview)
  • machine learning processes
    • supervised learning
    • unsupervised learning
      • clustering
    • reinforcement learning
    • evolutionary algorithms
    • validation split
  • machine learning models
    • linear regression
    • logistic regression
    • decision tree
    • random forest
    • support vector machine
    • feature scaling
    • linear discriminant analysis
    • neural network (overview)
      • neural network architectures
        • ​fully-connected neural network
        • recurrent neural network
        • convolutional neural network
• visualizing and understanding neural networks
  • generative adversarial networks (GANs)
  • heat maps
  • class activation mapping(CAM)
• 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
    • mean squared error
    • cross entropy
  • optimization algorithms 
    • ADAM 
    • momentum (Nesterov)
    • stochastic gradient descent
    • mini-batch gradient descent
  • regularisation
    • linear and quadratic
    • batch normalization 
  • ensembling
    • boosting
    • bagging
  • 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 registration
  • imputation
  • iteration
  • jaccard index
  • linear algebra
  • noise reduction
  • normalization
  • R (Programming language)
  • Python (Programming language)
  • segmentation 
  • semi-supervised learning
  • synthetic and augmented data
  • overfitting
  • transfer learning