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Computer-aided Diagnosis
Computer-aided detection (CADe), also called computer-aided diagnosis (CADx), are systems that assist doctors in the interpretation of medical imaging, medical images. Imaging techniques in X-ray, MRI, endoscopy, and Medical ultrasound, ultrasound diagnostics yield a great deal of information that the Radiology, 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. CAD also has potential future applications in digital pathology with the advent of whole-slide imaging and machine learning algorithms. So far its application has been limited to quantifying immunostaining but is also being investigated for the standard H&E stain. CAD is an interdisciplinary technology combining elements of artificial intelligence and computer visi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Projectional Radiography
Projectional radiography, also known as conventional radiography, is a form of radiography and medical imaging that produces two-dimensional images by X-ray radiation. The image acquisition is generally performed by radiographers, and the images are often examined by radiologists. Both the procedure and any resultant images are often simply called 'X-ray'. Plain radiography or roentgenography generally refers to projectional radiography (without the use of more advanced techniques such as CT scan, computed tomography that can generate 3D-images). ''Plain radiography'' can also refer to radiography without a radiocontrast agent or radiography that generates single static images, as contrasted to fluoroscopy, which are technically also projectional. Equipment X-ray generator Projectional radiographs generally use X-rays created by X-ray generators, which generate X-rays from X-ray tubes. Grid An anti-scatter grid may be placed between the patient and the detector to reduce the q ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lung Cancer
Lung cancer, also known as lung carcinoma, is a malignant tumor that begins in the lung. Lung cancer is caused by genetic damage to the DNA of cells in the airways, often caused by cigarette smoking or inhaling damaging chemicals. Damaged airway cells gain the ability to multiply unchecked, causing the growth of a tumor. Without treatment, tumors spread throughout the lung, damaging lung function. Eventually lung tumors metastasize, spreading to other parts of the body. Early lung cancer often has no symptoms and can only be detected by medical imaging. As the cancer progresses, most people experience nonspecific respiratory problems: coughing, shortness of breath, or chest pain. Other symptoms depend on the location and size of the tumor. Those suspected of having lung cancer typically undergo a series of imaging tests to determine the location and extent of any tumors. Definitive diagnosis of lung cancer requires a biopsy of the suspected tumor be examined by a patholo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-nearest Neighbors Algorithm
In statistics, the ''k''-nearest neighbors algorithm (''k''-NN) is a Non-parametric statistics, non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph Lawson Hodges Jr., Joseph Hodges in 1951, and later expanded by Thomas M. Cover, Thomas Cover. Most often, it is used for statistical classification, classification, as a ''k''-NN classifier, the output of which is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its ''k'' nearest neighbors (''k'' is a positive integer, typically small). If ''k'' = 1, then the object is simply assigned to the class of that single nearest neighbor. The ''k''-NN algorithm can also be generalized for regression analysis, regression. In ''-NN regression'', also known as ''nearest neighbor smoothing'', the output is the property value for the object. This value is the average of the values of ''k'' nearest neighbo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Image Segmentation
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects (Set (mathematics), sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.Linda Shapiro, Linda G. Shapiro and George C. Stockman (2001): "Computer Vision", pp 279–325, New Jersey, Prentice-Hall, Image segmentation is typically used to locate objects and Boundary tracing, boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of Contour line, contours extracted from the image (see edge detection). Each of the pixels in a region ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
DICOM
Digital Imaging and Communications in Medicine (DICOM) is a technical standard for the digital storage and Medical image sharing, transmission of medical images and related information. It includes a file format definition, which specifies the structure of a DICOM file, as well as a network communication protocol that uses Internet protocol suite, TCP/IP to communicate between systems. The primary purpose of the standard is to facilitate communication between the software and Computer hardware, hardware entities involved in medical imaging, especially those that are created by different manufacturers. Entities that utilize DICOM files include components of Picture archiving and communication system, picture archiving and communication systems (PACS), such as Modality (medical imaging), imaging machines (modalities), Radiological information system, radiological information systems (RIS), Image scanner, scanners, Printer (computing), printers, Server (computing), computing servers, a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Pattern Recognition
Pattern recognition is the task of assigning a class to an observation based on patterns extracted from data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess PR capabilities but their primary function is to distinguish and create emergent patterns. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. Pattern recognition systems are commonly trained from labeled "training" data. When no labeled data are available, other algorithms can be used to discover previously unknown patterns. KDD and data mining have a larger focus on unsupervised methods and str ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Mining
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the " knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (''mining'') of data itself. It also is a buzzwo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Richard M
Richard is a male given name. It originates, via Old French, from Frankish language, Old Frankish and is a Compound (linguistics), compound of the words descending from Proto-Germanic language, Proto-Germanic ''*rīk-'' 'ruler, leader, king' and ''*hardu-'' 'strong, brave, hardy', and it therefore means 'strong in rule'. Nicknames include "Richie", "Dick (nickname), Dick", "Dickon", "Dickie (name), Dickie", "Rich (given name), Rich", "Rick (given name), Rick", "Rico (name), Rico", "Ricky (given name), Ricky", and more. Richard is a common English (the name was introduced into England by the Normans), German and French male name. It's also used in many more languages, particularly Germanic, such as Norwegian, Danish, Swedish, Icelandic, and Dutch, as well as other languages including Irish, Scottish, Welsh and Finnish. Richard is cognate with variants of the name in other European languages, such as the Swedish "Rickard", the Portuguese and Spanish "Ricardo" and the Italian "Ricc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
CADUCEUS (expert System)
CADUCEUS was a medical expert system, an early type of recommender system - by Harry Pople of the University of Pittsburgh. Finished in the mid-1980s, it was built on the Internist-I, INTERNIST-1 algorithm (1972-1973). In its time, CADUCEUS was described as the "most knowledge-intensive expert system in existence".The Fifth Generation. Edward A. Feigenbaum and Pamela McCorduck. Addison-Wesley, Reading, Ma 01867, 275 Pp. Feb 1, 1984 CADUCEUS eventually could diagnose up to 1000 different diseases. The knowledge base was built on Pople's years of interviews with Jack Myers (biologist), Dr. Jack Meyers, one of the top internal medicine diagnosticians and a professor at the University of Pittsburgh. Their motivation was to improve on MYCIN, a recommender which focused on blood-borne infectious bacteria and instead embrace all internal medicine. While CADUCEUS worked using an inference engine similar to MYCIN's, it made a number of changes. As there can be a number of simultaneous dis ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Internist-I
INTERNIST-I (or INTERNIST-1) was a broad-based computer-assisted decision tree developed in the early 1970s at the University of Pittsburgh as an educational experiment. The INTERNIST system was designed primarily by AI pioneer and Computer Scientist Harry Pople to capture the diagnostic expertise of Jack D. Myers, chairman of internal medicine in the University of Pittsburgh School of Medicine. The Division of Research Resources and the National Library of Medicine funded INTERNIST-I. Other major collaborators on the project included Randolph A. Miller and Kenneth "Casey" Quayle, who did much of the implementation of INTERNIST and its successors. Development INTERNIST-I followed the DIALOG system as its successor. Over a decade, INTERNIST-I played a central role in the Pittsburgh course titled "The Logic of Problem-Solving in Clinical Diagnosis." Fourth-year medical students in the course collaborated with faculty experts to handle much of the data entry and system updates. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Expert Systems
In artificial intelligence (AI), an expert system is a computer system emulating the decision-making ability of a human expert. Expert systems are designed to solve complex problems by Automated reasoning system, reasoning through bodies of knowledge, represented mainly as Rule-based system, if–then rules rather than through conventional procedural programming code. Expert systems were among the first truly successful forms of AI software. They were created in the 1970s and then proliferated in the 1980s, being then widely regarded as the future of AI — before the advent of successful artificial neural networks. An expert system is divided into two subsystems: 1) a ''knowledge base'', which represents facts and rules; and 2) an ''inference engine'', which applies the rules to the known facts to deduce new facts, and can include explaining and debugging abilities. History Early development Soon after the dawn of modern computers in the late 1940s and early 1950s, researche ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
