Anomaly detection
Anomaly detection uses a large number of normal examples to train an algorithm which detects what is normal (based on the training examples) and what is not normal. Anomaly detection has features of both supervised and unsupervised learning, and is applicable to Radiology as it’s important to differentiate the normal from the abnormal
Anomaly detection is especially useful when there are many different “types” of anomalies as it’s hard for any algorithm to learn from examples what different types of anomalies should look like and future anomalies may not show similarities to anomalies in any training data.
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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
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optimization algorithms
- ADAM
- momentum (Nesterov)
- stochastic gradient descent
- mini-batch gradient descent
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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
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- linear algebra
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- R (Programming language)
- Python (Programming language)
- segmentation
- semi-supervised learning
- synthetic and augmented data
- overfitting
- transfer learning
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