MeningiomaMeningioma, also known as meningeal tumor, is typically a slow-growing tumor that forms from the meninges, the membranous layers surrounding the brain and spinal cord. Symptoms depend on the location and occur as a result of the tumor pressing on nearby tissue. Many cases never produce symptoms. Occasionally seizures, dementia, trouble talking, vision problems, one sided weakness, or loss of bladder control may occur. Risk factors include exposure to ionizing radiation such as during radiation therapy, a family history of the condition, and neurofibromatosis type 2.
GlioblastomaGlioblastoma, previously known as glioblastoma multiforme (GBM), is the most aggressive and most common type of cancer that originates in the brain, and has very poor prognosis for survival. Initial signs and symptoms of glioblastoma are nonspecific. They may include headaches, personality changes, nausea, and symptoms similar to those of a stroke. Symptoms often worsen rapidly and may progress to unconsciousness. The cause of most cases of glioblastoma is not known.
Decision tree learningDecision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.
HistopathologyHistopathology (compound of three Greek words: ἱστός histos 'tissue', πάθος pathos 'suffering', and -λογία -logia 'study of') refers to the microscopic examination of tissue in order to study the manifestations of disease. Specifically, in clinical medicine, histopathology refers to the examination of a biopsy or surgical specimen by a pathologist, after the specimen has been processed and histological sections have been placed onto glass slides. In contrast, cytopathology examines free cells or tissue micro-fragments (as "cell blocks").
Ovarian cancerOvarian cancer is a cancerous tumor of an ovary. It may originate from the ovary itself or more commonly from communicating nearby structures such as fallopian tubes or the inner lining of the abdomen. The ovary is made up of three different cell types including epithelial cells, germ cells, and stromal cells. When these cells become abnormal, they have the ability to divide and form tumors. These cells can also invade or spread to other parts of the body. When this process begins, there may be no or only vague symptoms.
Sampling (statistics)In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.
Surgical pathologySurgical pathology is the most significant and time-consuming area of practice for most anatomical pathologists. Surgical pathology involves gross and microscopic examination of surgical specimens, as well as biopsies submitted by surgeons and non-surgeons such as general internists, medical subspecialists, dermatologists, and interventional radiologists. The practice of surgical pathology allows for definitive diagnosis of disease (or lack thereof) in any case where tissue is surgically removed from a patient.
Decision treeA decision tree is a decision support hierarchical model that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.
Random forestRandom forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the mean or average prediction of the individual trees is returned. Random decision forests correct for decision trees' habit of overfitting to their training set.
Ensemble learningIn statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Unlike a statistical ensemble in statistical mechanics, which is usually infinite, a machine learning ensemble consists of only a concrete finite set of alternative models, but typically allows for much more flexible structure to exist among those alternatives.
