Articles
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92 results
Article
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 maximises these ...
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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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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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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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Support vector machine (machine learning)
The support vector machine (SVM) is a supervised learning algorithm used to separate groups of data with a margin or plane which is made as well as possible to ensure it is more likely to generalise well to examples it has never seen before. In the case of a two feature data set a margin or line...
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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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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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Centring
Centring is a statistical operation on data. In the context of neural networks for image classification related tasks, it implies intensity normalisation across images in training data sets. In the context of neural networks specifically for x-ray based images it therefore implies correction for...
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Semi-supervised learning (machine learning)
Semi-supervised learning is an approach to machine learning which uses some labelled data and some data without labels to train models. This approach can be useful to overcome the problem of insufficient quantities of labelled data. Some consider it to be a variation of supervised learning, whil...
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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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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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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 utilised 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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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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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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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 neurones, in one layer are connected to the neurones in the next layer.
While this type of algorithm is commonly applied to some types of data, in practice this...
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