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 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:
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Article
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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Heat map
Heat maps are visual representations of data in matrices with colors. Two dimensions of the data are captured by the location of a point (i.e., a map) and a third dimension is represented by the color of the point (i.e., the value).
Some nuclear medicine studies are technically examples of heat...
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Hyperparameter (machine learning)
Hyperparameters are specific aspects of a machine learning algorithm that are chosen before the algorithm runs on data. These hyperparameters are model specific e.g. they would typically include the number of epochs for a deep learning model or the number of branches in a decision tree model. Th...
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Generalisability
Generalisability in machine learning models represents how well the models can be adapted to new example datasets.
Evaluating generalisability of machine learning applications is crucial as this has profound implications for their clinical adaptability. Briefly, two main techniques are used fo...
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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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Transfer learning
The concept of transfer learning in artificial neural networks is taking knowledge acquired from training on one particular domain and applying it in learning a separate task.
In recent years, a well-established paradigm has been to pre-train models using large-scale data (e.g., ImageNet) and t...
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Imputation
Imputation refers to statistical methods for creating data when it is missing from a data set. Missing data is often not random (and can therefore lead to different forms of bias). Imputation theoretically improves research outcomes as opposed to simply discarding incomplete data subsets. Severa...
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Kernel (computing)
A kernel, in terms of general computing terminology, is the main part of a specific software. The term, unless otherwise specified, refers to the main part of the operating system software and some sources even use it interchangeably with operating system. This term can also describe certain mac...
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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...
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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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Semi-supervised learning (machine learning)
Semi-supervised learning is an approach to machine learning which uses some labeled data and some data without labels to train models. This approach can be useful to overcome the problem of insufficient quantities of labeled data. Some consider it to be a variation of supervised learning, whilst...
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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...
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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...
Article
Quantitative imaging biomarker
Quantitative imaging biomarkers are validated, standardized characteristics based on quantifiable features of biomedical imaging that can be reliably and objectively measured on a ratio or interval scale. The utility of quantitative imaging biomarkers lies in providing information beyond what ca...
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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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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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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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Regularisation (Regularization)
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.
In the context of radiology, a common model type used to interpret images is the convolutional neural network...
Article
Selection bias
Selection bias is a type of bias created when the data sampled is not representative of the 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 bi...
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