Articles
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91 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) has been defined by some as the "branch of computer science dealing with the simulation of intelligent behavior 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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Artificial Intelligence (AI) TI-RADS
AI TI-RADS (Artificial Intelligence Thyroid Imaging Reporting and Data System) 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...
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Autoencoder
Autoencoders are an unsupervised learning technique in which artificial neural networks are used to learn to produce a compressed representation of the input data.
Essentially, autoencoding is a data compression algorithm where the compression and decompression functions are learned automatical...
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Automation bias
Automation bias is a form of cognitive bias occurring when humans overvalue information produced by an automated, usually computerized, system. Users of automated systems can fail to understand or ignore illogical or incorrect information produced by computer systems.
Computer programs may crea...
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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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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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Bayes' factor
A Bayes' factor is a number that quantifies the relative likelihood of two models or hypotheses to each other if made into a ratio e.g. if two models are equally likely based on the prior evidence ( or there is no prior evidence) then the Bayes factor would be one.
Such factors have several use...
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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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Boosting
Boosting is an ensemble technique that creates increasingly complex algorithms from building blocks of relatively simple decision rules for binary classification tasks. This is achieved by sequentially training new models (or 'weak' learners) which focus on examples that were classified incorre...
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Centering
Centering is a statistical operation on data. In the context of neural networks for image classification related tasks, it implies intensity normalization across images in training data sets. In the context of neural networks specifically for x-ray based images it therefore implies correction fo...
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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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Class activation mapping (CAM)
Class activation mapping is a method to generate heatmaps of images that show which areas were of high importance in terms of a neural networks for image classification. There are several variations on the method including Score-CAM and Grad-CAM (Gradient Weighted Class Activation Mapping). The ...
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Clustering
Clustering, also known as cluster analysis, is a machine learning technique designed to group similar data points together. Since the data points do not necessarily have to be labeled, clustering is an example of unsupervised learning. Clustering in machine learning should not be confused with d...
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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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Computer vision
Computer vision is a field concerned with the creation of generalized automated computer insight into visual data i.e. making computers see. Although often understood as a field within computer science, the field actually involves work in informatics, various fields of engineering and neuroscien...
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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....
Article
Convolution
Convolution is a mathematical concept that implies the product of two functions. In practical terms for radiology, convolution implies the application of a mathematical operation to a signal such that a different signal is produced. Convolutions are applied in image processing for CTs and MRIs.
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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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