Medical imagingMedical imaging is the technique and process of imaging the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology). Medical imaging seeks to reveal internal structures hidden by the skin and bones, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities.
Feature (machine learning)In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.
Slash distributionIn probability theory, the slash distribution is the probability distribution of a standard normal variate divided by an independent standard uniform variate. In other words, if the random variable Z has a normal distribution with zero mean and unit variance, the random variable U has a uniform distribution on [0,1] and Z and U are statistically independent, then the random variable X = Z / U has a slash distribution. The slash distribution is an example of a ratio distribution. The distribution was named by William H.
Renal biopsyRenal biopsy (also kidney biopsy) is a medical procedure in which a small piece of kidney is removed from the body for examination, usually under a microscope. Microscopic examination of the tissue can provide information needed to diagnose, monitor or treat problems of the kidney. A renal biopsy can be targeted to a particular lesion, for example a tumour arising from the kidney (targeted renal biopsy). More commonly, however, the biopsy is non-targeted as medical conditions affecting the kidney typically involve all kidney tissue indiscriminately.
Kidney cancerKidney cancer, also known as renal cancer, is a group of cancers that starts in the kidney. Symptoms may include blood in the urine, lump in the abdomen, or back pain. Fever, weight loss, and tiredness may also occur. Complications can include spread to the lungs or brain. The main types of kidney cancer are renal cell cancer (RCC), transitional cell cancer (TCC), and Wilms' tumor. RCC makes up approximately 80% of kidney cancers, and TCC accounts for most of the rest.
Feature selectionFeature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Stylometry and DNA microarray analysis are two cases where feature selection is used. It should be distinguished from feature extraction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/users, shorter training times, to avoid the curse of dimensionality, improve data's compatibility with a learning model class, encode inherent symmetries present in the input space.
Contrast-induced nephropathyContrast-induced nephropathy (CIN) is a purported form of kidney damage in which there has been recent exposure to medical imaging contrast material without another clear cause for the acute kidney injury. Despite extensive speculation, the actual occurrence of contrast-induced nephropathy has not been demonstrated in the literature. Analysis of observational studies has shown that radiocontrast use in CT scanning is not causally related to changes in kidney function.
Principal component analysisPrincipal component analysis (PCA) is a popular technique for analyzing large datasets containing a high number of dimensions/features per observation, increasing the interpretability of data while preserving the maximum amount of information, and enabling the visualization of multidimensional data. Formally, PCA is a statistical technique for reducing the dimensionality of a dataset. This is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.
Multiclass classificationIn machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.
Statistical classificationIn statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.
