# Articles

Articles are a collaborative effort to provide a single canonical page on all topics relevant to the practice of radiology. As such, articles are written and continuously improved upon by countless contributing members. Our dedicated editors oversee each edit for accuracy and style. Find out more about articles.

92 results

Article

#### Machine learning

Machine learning is a specific practical application of computer science and mathematics that allows computers to extrapolate information based on observed patterns without explicit programming. A defining characteristic of machine learning programs is the improved performance at tasks such as c...

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#### Supervised learning (machine learning)

Supervised learning is the most common type of machine learning algorithm used in medical imaging research. It involves training an algorithm from a set of images or data where the output labels are already known 1.
Supervised learning is broken into two subcategories, classification and regres...

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#### Convolutional neural network

A convolutional neural network (CNN) is a particular implementation of a neural network used in deep learning that exclusively processes array data such as images, and is thus frequently used in machine learning applications targeted at medical images 1.
Architecture
A convolutional neural net...

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#### Evolutionary algorithms (machine learning)

Evolutionary algorithms are one of the main types of algorithms used in machine learning, emulating natural selection whereby pseudorandom variations in the algorithm are measured against selective pressures created by functions. The more successful algorithms are then used as the 'parents' of t...

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#### Reinforcement learning (machine learning)

Reinforcement learning is one of the main algorithms used in machine learning in the context of an agent in an environment. In each timestep, this agent takes in information from their environment and performs an action. Certain actions reward the agent.
Reinforcement learning maximises these ...

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#### Unsupervised learning (machine learning)

Unsupervised learning is one of the main types of algorithms used in machine learning.
Unsupervised learning algorithms are used on datasets where output labels are not provided. Hence, instead of trying to predict a particular output for each input, these algorithms attempt to discover the un...

Article

#### Batch size (machine learning)

Batch size is a term used in machine learning and refers to the number of training examples utilised in one iteration. The batch size can be one of three options:
batch mode: where the batch size is equal to the total dataset thus making the iteration and epoch values equivalent
mini-batch mod...

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#### Epoch (machine learning)

An epoch is a term used in machine learning and indicates the number of passes of the entire training dataset the machine learning algorithm has completed. Datasets are usually grouped into batches (especially when the amount of data is very large). Some people use the term iteration loosely and...

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#### Iteration (machine learning)

An iteration is a term used in machine learning and indicates the number of times the algorithm's parameters are updated. Exactly what this means will be context dependent. A typical example of a single iteration of training of a neural network would include the following steps:
processing the ...

Article

#### Cost function (machine learning)

A cost function is a mechanism utilised in supervised machine learning, the cost function returns the error between predicted outcomes compared with the actual outcomes. The aim of supervised machine learning is to minimise the overall cost, thus optimising the correlation of the model to the sy...

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#### Backpropagation (machine learning)

Backpropagation in supervised machine learning is the process used to calculate the gradient of the error function associated with each parameter weighting within a convoluted neural network (CNN). Essentially, the gradient estimates how the system parameters should change in order to optimise t...

Article

#### Ensembling

Ensembling (sometimes ensemble learning) is a class of meta-algorithmic techniques where multiple models are trained and their results are aggregated to improve classification performance. It is effective in a wide variety of problems.
Two commonly used methods are:
boosting: a method of wei...

Article

#### Neural network (overview)

Artificial neural networks are a powerful type of model capable of processing many types of data. Initially inspired by the connections between biological neural networks, modern artificial neural networks only bear slight resemblances at a high level to their biological counterparts. Nonetheles...

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#### Artificial intelligence

Artificial intelligence (AI) has been defined by some as the "branch of computer science dealing with the simulation of intelligent behaviour in computers" 1, however, the precise definition is a matter of debate among experts.
An alternative definition is the branch of computer science dedicat...

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#### Rule-based expert systems

A rule-based expert system is the simplest form of artificial intelligence and uses prescribed knowledge-based rules to solve a problem 1. The aim of the expert system is to take knowledge from a human expert and convert this into a number of hardcoded rules to apply to the input data. In their...

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#### Radiomics

Radiomics (as applied to radiology) is a field of medical study that aims to extract a large number of quantitative features from medical images using data characterisation algorithms. The data is assessed for improved decision support. It has the potential to uncover disease characteristics tha...

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#### Linear regression (machine learning)

Linear regression in machine learning is a form of supervised learning, derived from the linear regression models in statistics. It operates under the assumption that two variables have a linear relationship, therefore, can calculate the value of an output variable based on the input variable. L...

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#### Decision tree (machine learning)

The decision tree model in machine learning is an algorithm that offers choices based on characteristics of the data. It follows 'branch node theory' in which each branch will represent a variable alongside a decision.
Often decision tree models will be expressed in the following rule format: ...

Article

#### Logistic regression (machine learning)

Logistic regression in machine learning is a classification model which predicts the probabilities of binary outcomes, as opposed to linear regression which predicts actual values.
Logistic regression outputs are constrained between 0 and 1, and hence is a popular simple classification method ...

Article

#### Principal component analysis

Principal component analysis is a mathematical transformation that can be understood in two parts:
the transformation maps multivariable data (Nold dimensions) into a new coordinate system (Nnew dimensions) with minimal loss of information.
data projected on the first dimension of the new coor...