Search results for “also”
31 results found
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Cybersecurity
Cybersecurity is the protection of digital data, software and hardware from risks including attacks or other problems related to their integrity and/or data confidentiality. Cybersecurity may utilize many different types of tools and protocols including encryption, firewalls and other infrastruc...
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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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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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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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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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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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Noise reduction
Noise reduction, also known as noise suppression or denoising, commonly refers to the various algorithmic techniques to reduce noise in digital images once they are created although a few sources use the term more broadly to imply anything that reduces noise. In digital image processing various ...
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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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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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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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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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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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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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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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Synthetic and augmented data
In the context of radiological images, synthetic and augmented data are data that are not completely generated by direct measurement from patients.
Machine learning models improve with increased data. However, there is a relative lack of open, free available radiology data sets. Issues of patie...
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Federated learning
Federated learning, also known as distributed learning, is a technique that facilitates the creation of robust artificial intelligence models where data is trained on local devices (nodes) that then transfer weights to a central model. Models can potentially be trained using larger and/or more d...
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Generative adversarial network
Generative adversarial networks (GANs) are an elegant deep learning approach to generating artificial data that is indistinguishable from real data. Two neural networks are paired off against one another (adversaries). The first network generates artificial data to reproduce real data. The secon...
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