# Items tagged “ai”

21 results found

Article

#### 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...

Article

#### 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...

Article

#### Support vector machine (machine learning)

The support vector machine (SVM) is a supervised learning algorithm used to separate groups of data with a margin which is as clear as possible to ensure it is more likely to generalize well to examples it has never seen before.
There are three different margins to achieve separation of the two...

Article

#### Support vector machine (machine learning)

The support vector machine (SVM) is a supervised learning algorithm used to separate groups of data with a margin which is as clear as possible to ensure it is more likely to generalize well to examples it has never seen before.
There are three different margins to achieve separation of the two...

Article

#### 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...

Article

#### 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...

Article

#### Gradient descent

The gradient descent algorithm aims to minimize a cost function (degree of predicting error) of a model in order to produce a model that gives the most accurate predictions. Gradient descent is by far the most commonly used algorithm in machine learning, and is usually the first algorithm most p...

Article

#### Stochastic gradient descent

Stochastic gradient descent is an optimization algorithm which improves the efficiency of the gradient descent algorithm. Similar to batch gradient descent, stochastic gradient descent performs a series of steps to minimize a cost function. Unlike batch gradient descent, which is computationally...

Article

#### Mini-batch gradient descent

The mini-batch gradient descent is a technique that combines properties from batch gradient descent and also stochastic gradient descent to optimize efficiency and accuracy of the gradient descent algorithm. In each iteration, a certain number of examples (a batch) within a data set will undergo...

Article

#### 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

#### Anomaly detection

Anomaly detection uses a large number of normal examples to train an algorithm which detects what is normal (based on the training examples) and what is not normal. Anomaly detection has features of both supervised and unsupervised learning, and is applicable to Radiology as it’s important to di...

Article

#### Dimensionality reduction

Dimensionality reduction is the process of combining the information from a large number of features to a create a smaller number of features, either to reduce the computational cost or to visualize the data.
In order to achieve the most accurate result, it is often required to have many featur...

Article

#### Validation split (machine learning)

In order to ensure that machine learning models are able to generalize well to new data not seen before by the model, is it important to have train, test, and cross-validation split for the original set of data to obtain the best possible predictive model.
Training Set
When conducting machine ...

Article

#### Augmentation

Augmentation is a process of artificial data generation, which produces a greater volume of data, and thus increasing the likelihood of obtaining higher predictive accuracy of a predictive model.
Usually, a higher volume of data is likely to yield better predictive and more accurate models from...

Article

#### Natural language processing

Natural language processing (NLP) is an area of active research in artificial intelligence concerned with human languages. Natural language processing programs use human written text or human speech as data for analysis. The goals of natural language processing programs can vary from generating ...

Article

#### 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...

Article

#### Python (programming language)

Python is a high-level, general-purpose computer programming language. Initially, Python was created by Dutch computer programmer Guido van Rossum and was first released in 1991. The version 3.7.4 (which is the most recent stable release as of July 2019) Python language has objects and associat...

Article

#### R (Programming Language)

R is a programming language and free open-source software environment for statistical computing and graphics supported by the R Foundation. It is freely available under the GNU General Public License. R is a highly popular language for programming in statistics in general and bio-statistics in p...