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
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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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Machine learning
Machine learning is a specific practical application of computer science and mathematics that allows computers to extrapolate information based on observed patterns without explicit programming. A defining characteristic of machine learning programs is the improved performance at tasks such as c...
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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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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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Underfitting
Underfitting in statistical and machine learning modeling is the counterpart of overfitting.
It happens when a model is not complex enough to accurately capture relationships between a dataset’s features and a target variable, i.e. the network is struggling with learning the patterns in the dat...
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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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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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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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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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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...
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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 ...
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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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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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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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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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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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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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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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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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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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