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 edited by countless contributing members over a period of time. A global group of dedicated editors oversee accuracy, consulting with expert advisers, and constantly reviewing additions.

44 results found
Article

Activation function

In neural networks, activation functions perform a transformation on a weighted sum of inputs plus biases to a neuron in order to compute its output. Using a biological analogy, the activation function determines the “firing rate” of a neuron in response to an input or stimulus. These functions...
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Artificial intelligence

Artificial intelligence (AI) is the "branch of computer science dealing with the simulation of intelligent behavior in computers" 1. AI algorithms and in particular deep learning (part of machine learning) aim to either assist humans with solving a problem or solve the problem without human inpu...
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Artificial Intelligence (AI) TI-RADS

AI TI-RADS is a data-driven analysis and revision of the 2017 ACR TI-RADS 1. Published in May 2019 2, this had the intention of simplifying categorization and improving specificity while maintaining high sensitivity. This system used a training set of 1325 nodules with known cytology and a genet...
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Augmentation

Augmentation is a process of artificial data generation, which produces a greater volume of data, and thus increasing the likelihood of obtaining higher predictive accuracy of a predictive model. Usually, a higher volume of data is likely to yield better predictive and more accurate models from...
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Backpropagation (machine learning)

Backpropagation in supervised machine learning is the process used to calculate the gradient associated with each parameter weighting. Essentially, the gradient estimates how the system parameters should change in order to optimize the network overall 1,2.
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Bagging

Bagging is a term often used in the fields of machine learning, data science and computational statistics that refers to bootstrap aggregation. Bootstrapped aggregation of data can be employed in many different AI (artificial intelligence) algorithms, and is often a necessary step to making rand...
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Batch size (machine learning)

Batch size is a term used in machine learning and refers to the number of training examples utilized 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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Computer aided diagnosis

Computer Aided Diagnosis (CAD) is the use of a computer generated output as an assisting tool for a clinician to make a diagnosis. It is different to automated computer diagnosis, in which the end diagnosis is based on a computer algorithm only. Computer aided diagnosis has already been used ext...
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Confusion matrix

Confusion matrices, a key tool to evaluate machine learning algorithm performance in classification, are a statistical tool. Contingency tables, a type of confusion matrix, are used in the evaluation of many diagnostic exams for sensitivity, specificity, positive and negative predictive values....
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Convolutional neural network

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

A cost function is a mechanism utilized 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 minimize the overall cost, thus optimizing the correlation of the model to the sy...
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Cross entropy

Cross entropy is a measure of the degree of inequality between two probability distributions. In the context of supervised learning, one of these distributions represents the “true” label for a training example, where the correct responses are assigned a value of 100%. Machine learning If p(x)...
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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: ...
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DICOM to bitmap conversion

DICOM to bitmap conversion describes the process of converting medical images stored within DICOM file format to raw pixel data. Computer vision techniques for processing image data usually work on raw pixel values and therefore this conversion is required before further processing may take plac...
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Ensembling

Ensembling (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 weighting the...
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Epoch (machine learning)

An epoch is a term used in machine learning and indicates the number of passes through the entire training dataset the machine learning algorithm has completed.  If the batch size is the whole training dataset (batch mode) then batch size and epoch are equivalent. 
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Evolutionary algorithms (machine learning)

Evolutionary algorithms are one of the main algorithms used in machine learning, emulating natural selection whereby pseudorandom changes in the algorithm are measured against selective pressures. The more successful algorithms are then used as the 'parents' of the next generation of algorithms. 
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Feature scaling

Feature scaling a preprocessing technique that is used to standardize the range of values in data features, making sure that the features are on a similar scale. It is used when the range of values of a certain feature is too variable and contains extreme values as most algorithms perform poorly...
Article

Imaging data sets (artificial intelligence)

The aggregation of an imaging data set is a critical step in building artificial intelligence (AI) for radiology. Imaging data sets are used in various ways including training and/or testing algorithms. Many data sets for building convolutional neural networks for image identification involve at...
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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 ...
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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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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 ...
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Loss function

A loss function is a mathematical function commonly used in statistics. Loss functions are frequently used to create machine learning algorithms. Loss functions are used in machine learning to measure the mathematical distance between predicted values and actual real values. For example in ima...
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Machine learning

Machine learning is an avenue of computer science that can extrapolate information based on observed patterns without explicit programming.The defining characteristic of machine learning programs is the improved performance when more data, known as training data, is processed. Machine learning ...
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Machine learning processes

The specifics of how a machine learning algorithm is trained to recognize certain features and thereby become able to make accurate predictions on new examples varies depending on the type of data being used and the algorithm architecture. Four of the most commonly used learning processes are: ...
Article

Mean squared error

Mean squared error is a specific type of loss function. Mean square error is calculated by the average, specifically the mean, of errors that have been squared from data as it relates to a function ( often a regression line).  The utility of mean square error comes from the fact that squared nu...
Article

Models (machine learning)

Each machine learning model will vary whilst being determined in part by the type of problem being solved. Although much of the recent work in the field of image processing generally, and more specifically radiology, has focused on convolutional neural networks, a type of neural network, a numbe...
Article

Natural language processing

Natural language processing (NLP) is an area of active research in artificial intelligence concerned with human languages. NLP programs use human written text or human speech as data for analysis. The goals of NLP programs can vary from generating insights from texts or recorded speech to genera...
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Neural network architectures

Artificial neural networks can be broadly divided into different architectures, feedforward or recurrent neural architectures. Feedforward neural networks are more readily conceptualised in 'layers'. The first layer of the neural network is merely the inputs of each sample, and each neuron in e...
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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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Overfitting

Overfitting is a problem in machine learning that introduces errors based on noise and meaningless data into prediction or classification. Overfitting tends to happen in cases where training data sets are either of insufficient size or training data sets include parameters and/or unrelated featu...
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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...
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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-characterization algorithms. The data is assessed for improved decision support. It has the potential to uncover disease characteristics tha...
Article

Random forest (machine learning)

Random Forest also known as random decision forests are a specific type of ensembling algorithm that utilizes a combination of decision trees based on subsets of a dataset. A random forest algorithm does not make a decision tree of smaller decision trees, but rather utilizes decision trees in pa...
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Regularisation

Regularisation is a process of reducing the complexity of a model through the inclusion of an additional parameter as in order to reduce the overfitting of a model to the training data.
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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 maximizes these ...
Article

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...
Article

Selection bias

Selection bias is a type of bias created when the data sampled is not representative of data of the population or group that a study or model aims to make a prediction about. Selection bias is the result of systematic errors in data selection and collection. Practically speaking selection bias o...
Article

Single linear regression

Single linear regression, also known as simple linear regression, in statistics, is a technique that maps a relationship between one independent and one dependent variable into a first-degree polynomial. Linear regression is the simplest example of curve fitting, a type of mathematical problem i...
Article

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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Support vector machine (machine learning)

The support vector machine (SVM) is a supervised learning algorithm used to separate groups of data with a margin which is as clear as possible to ensure it is more likely to generalize well to examples it has never seen before. There are three different margins to achieve separation of the two...
Article

Synthetic and augmented data

In the context of radiological images, synthetic and augmented data are data that are not completely generated by direct measurement from patients. Machine learning by definition improves with increased data, however, there is a relative lack of open, free available radiology data sets. Issues ...
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Training, testing and validation datasets

The division of the input data into training, testing and validation sets is crucial in the creation of robust machine learning algorithms. Firstly, machine learning algorithms require a training set to be trained on. Each iteration, it calculates the difference between the predicted and actual ...
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Unsupervised learning (machine learning)

Unsupervised learning is one of the main 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 underlying ...

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