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.
91 results found
Article
Hebbian learning
Hebbian learning describes a type of activity-dependent modification of the strength of synaptic transmission at pre-existing synapses which plays a central role in the capacity of the brain to convert transient experiences into memory. According to Hebb et al 1, two cells or systems of cells th...
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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 from automated computer diagnosis, in which the end diagnosis is based on a computer algorithm only.
As an early form of artificial intelligence, co...
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ImageNet dataset
The ImageNet is an extensive image database that has been instrumental in advancing computer vision and deep learning research. It contains more than 14 million, hand-annotated images classified into more than 20,000 categories. In at least one million of the images, bounding boxes are also prov...
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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 work in the field of image processing generally, and more specifically radiology, has focussed on convolutional neural networks, a type of neural network, a number of o...
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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...
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Bayes' theorem
Bayes' theorem, also known as Bayes' rule or Bayes' law, is a theorem in statistics that describes the probability of one event or condition as it relates to another known event or condition. Mathematically, the theory can be expressed as follows: P(A|B) = (P(B|A) x P(A) )/P(B), where given that...
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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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Fully connected neural network
Fully connected neural networks (FCNNs) are a type of artificial neural network where the architecture is such that all the nodes, or neurons, in one layer are connected to the neurons in the next layer.
While this type of algorithm is commonly applied to some types of data, in practice this t...
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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 ...
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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 optimize t...
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Explainable artificial intelligence
Explainable artificial intelligence usually refers to narrow artificial intelligence models made with methods that enable and enhance human understanding of how the models reached outputs in each case. Many older AI models, e.g. decision trees, were inherently understandable in terms of how they...
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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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Loss function
A loss function is a mathematical function commonly used in statistics. Loss functions are frequently used to create machine learning algorithms.
The loss function computes the error for a single training example in contrast to a Cost function, which is the average of the loss functions from ea...
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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...
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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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CheckList for EvaluAtion of Radiomics research (CLEAR)
The CheckList for Evaluation of Radiomics Research (CLEAR) is a 58-item reporting guideline designed specifically for radiomics. It aims to improve the quality of reporting in radiomics research 1. CLEAR is endorsed by the European Society of Radiology (ESR) and the European Society of Medical I...
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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 or plane which is made as well as possible to ensure it is more likely to generalize well to examples it has never seen before. In the case of a two feature data set a margin or line...
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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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Linear discriminant analysis
Linear discriminant analysis (LDA) is a type of algorithmic model employed in machine learning in order to classify data. Unlike some other now popular models, linear discriminant analysis has been used for decades in both AI for radiology 1 and many other biomedical applications.
Linear discri...
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Information leakage
Information leakage is one of the common and important errors in data handling during all machine learning applications, including those in radiology. Briefly, it means the incomplete separation of the training, validation, and testing datasets, which can significantly change the apparent perfor...
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