Digital pathologyDigital pathology is a sub-field of pathology that focuses on data management based on information generated from digitized specimen slides. Through the use of computer-based technology, digital pathology utilizes virtual microscopy. Glass slides are converted into digital slides that can be viewed, managed, shared and analyzed on a computer monitor.
PathologyPathology is the study of the causes and effects of disease or injury. The word pathology also refers to the study of disease in general, incorporating a wide range of biology research fields and medical practices. However, when used in the context of modern medical treatment, the term is often used in a narrower fashion to refer to processes and tests that fall within the contemporary medical field of "general pathology", an area which includes a number of distinct but inter-related medical specialties that diagnose disease, mostly through analysis of tissue and human cell samples.
Medical diagnosisMedical diagnosis (abbreviated Dx, Dx, or Ds) is the process of determining which disease or condition explains a person's symptoms and signs. It is most often referred to as diagnosis with the medical context being implicit. The information required for diagnosis is typically collected from a history and physical examination of the person seeking medical care. Often, one or more diagnostic procedures, such as medical tests, are also done during the process. Sometimes the posthumous diagnosis is considered a kind of medical diagnosis.
Brain tumorA brain tumor occurs when abnormal cells form within the brain. There are two main types of tumors: malignant tumors and benign (non-cancerous) tumors. These can be further classified as primary tumors, which start within the brain, and secondary tumors, which most commonly have spread from tumors located outside the brain, known as brain metastasis tumors. All types of brain tumors may produce symptoms that vary depending on the size of the tumor and the part of the brain that is involved.
Slide projectorA slide projector is an opto-mechanical device for showing photographic slides. 35 mm slide projectors, direct descendants of the larger-format magic lantern, first came into widespread use during the 1950s as a form of occasional home entertainment; family members and friends would gather to view slide shows. Reversal film was much in use, and supplied slides snapped during vacations and at family events. Slide projectors were also widely used in educational and other institutional settings.
Differential diagnosisIn healthcare, a differential diagnosis (DDx) is a method of analysis of a patient's history and physical examination to arrive at the correct diagnosis. It involves distinguishing a particular disease or condition from others that present with similar clinical features. Differential diagnostic procedures are used by clinicians to diagnose the specific disease in a patient, or, at least, to consider any imminently life-threatening conditions. Often, each individual option of a possible disease is called a differential diagnosis (e.
Computer-aided diagnosisComputer-aided detection (CADe), also called computer-aided diagnosis (CADx), are systems that assist doctors in the interpretation of medical images. Imaging techniques in X-ray, MRI, Endoscopy, and ultrasound diagnostics yield a great deal of information that the radiologist or other medical professional has to analyze and evaluate comprehensively in a short time. CAD systems process digital images or videos for typical appearances and to highlight conspicuous sections, such as possible diseases, in order to offer input to support a decision taken by the professional.
Benign tumorA benign tumor is a mass of cells (tumor) that does not invade neighboring tissue or metastasize (spread throughout the body). Compared to malignant (cancerous) tumors, benign tumors generally have a slower growth rate. Benign tumors have relatively well differentiated cells. They are often surrounded by an outer surface (fibrous sheath of connective tissue) or stay contained within the epithelium. Common examples of benign tumors include moles and uterine fibroids. Some forms of benign tumors may be harmful to health.
Presentation slideA slide is a single page of a presentation. Collectively, a group of slides may be known as a slide deck. A slide show is an exposition of a series of slides or images in an electronic device or in a projection screen. Before the advent of the personal computer, a presentation slide could be a 35 mm slide viewed with a slide projector or a transparency viewed with an overhead projector. In the digital age, a slide most commonly refers to a single page developed using a presentation program such as MS PowerPoint, Apple Keynote, Google Slides, Apache OpenOffice or LibreOffice.
Brain metastasisA brain metastasis is a cancer that has metastasized (spread) to the brain from another location in the body and is therefore considered a secondary brain tumor. The metastasis typically shares a cancer cell type with the original site of the cancer. Metastasis is the most common cause of brain cancer, as primary tumors that originate in the brain are less common. The most common sites of primary cancer which metastasize to the brain are lung, breast, colon, kidney, and skin cancer.
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.
