Articles
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91 results found
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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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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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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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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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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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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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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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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...
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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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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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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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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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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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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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Optimization algorithms
Optimization algorithms are widely utilized mathematical functions that solve problems via the maximization or minimization of a function. These algorithms are used for a variety of purposes from patient scheduling to radiology.
Machine learning
Optimization algorithms are used in machine lea...
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Recurrent neural network
Recurrent neural networks (RNNs) are a form of a neural network that recognizes patterns in sequential information via contextual memory. Recurrent neural networks have been applied to many types of sequential information including text, speech, videos, music, genetic sequences and even clinical...
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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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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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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...
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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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