Computer-assisted Diagnosis
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Computer-assisted Diagnosis
Computer-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. 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 vision with radiological and pathology image proces ...
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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 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 quanti ...
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Lung Cancer
Lung cancer, also known as lung carcinoma (since about 98–99% of all lung cancers are carcinomas), is a malignant lung tumor characterized by uncontrolled cell growth in tissue (biology), tissues of the lung. Lung carcinomas derive from transformed, malignant cells that originate as epithelial cells, or from tissues composed of epithelial cells. Other lung cancers, such as the rare sarcomas of the lung, are generated by the malignant transformation of connective tissues (i.e. nerve, fat, muscle, bone), which arise from mesenchymal cells. Lymphomas and melanomas (from lymphoid and melanocyte cell lineages) can also rarely result in lung cancer. In time, this uncontrolled neoplasm, growth can metastasis, metastasize (spreading beyond the lung) either by direct extension, by entering the lymphatic circulation, or via hematogenous, bloodborne spread – into nearby tissue or other, more distant parts of the body. Most cancers that originate from within the lungs, known as primary ...
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K-nearest Neighbors Algorithm
In statistics, the ''k''-nearest neighbors algorithm (''k''-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression. In both cases, the input consists of the ''k'' closest training examples in a data set. The output depends on whether ''k''-NN is used for classification or regression: :* In ''k-NN classification'', the output 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. :* In ''k-NN regression'', the output is the property value for the object. This value is the average of the values of ''k'' nearest neighbors. If ''k'' = 1, then the output is simply assigned to the v ...
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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 ( 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 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 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 contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed ...
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DICOM
Digital Imaging and Communications in Medicine (DICOM) is the standard for the communication and management of medical imaging information and related data. DICOM is most commonly used for storing and transmitting medical images enabling the integration of medical imaging devices such as scanners, servers, workstations, printers, network hardware, and picture archiving and communication systems (PACS) from multiple manufacturers. It has been widely adopted by hospitals and is making inroads into smaller applications such as dentists' and doctors' offices. DICOM files can be exchanged between two entities that are capable of receiving image and patient data in DICOM format. The different devices come with DICOM Conformance Statements which state which DICOM classes they support. The standard includes a file format definition and a network communications protocol that uses TCP/IP to communicate between systems. The National Electrical Manufacturers Association (NEMA) holds the cop ...
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Pattern Recognition
Pattern recognition is the automated recognition of patterns and regularities in data. It 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. These activities can be viewed as two facets of the same field of application, and they have undergone substantial development over the past few decades. 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 stronger connection to business use. Pattern recognition focuses more on the s ...
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Richard M
Richard is a male given name. It originates, via Old French, from Old Frankish and is a compound of the words descending from Proto-Germanic ''*rīk-'' 'ruler, leader, king' and ''*hardu-'' 'strong, brave, hardy', and it therefore means 'strong in rule'. Nicknames include "Richie", " Dick", "Dickon", " Dickie", "Rich", "Rick", "Rico", "Ricky", and more. Richard is a common English, 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 Catalan "Ricard" and the Italian "Riccardo", among others (see comprehensive variant list below). People named Richard Multiple people with the same name * Richard Andersen (other) * Richard Anderson (other) * Richard Cartwright (other) * ...
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CADUCEUS (expert System)
CADUCEUS was a medical expert system finished in the mid-1980s (first begun in the 1970s- it took a long time to build the knowledge base) by Harry Pople (of the University of Pittsburgh), building on Pople's years of interviews with 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 (which focused on blood-borne infectious bacteria) to focus on more comprehensive issues than a narrow field like blood poisoning (though it would do it in a similar manner); instead embracing all internal medicine. CADUCEUS eventually could diagnose up to 1000 different diseases. While CADUCEUS worked using an inference engine similar to MYCIN's, it made a number of changes (like incorporating abductive reasoning) to deal with the additional complexity of internal disease- there can be a number of simultaneous diseases, and data is generally flawed and scarce. CADUCEUS has been described as the " ...
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Internist-I
{{Short description, Computer-assisted diagnostic software tool INTERNIST-I was a broad-based computer-assisted diagnostic tool developed in the early 1970s at the University of Pittsburgh as an educational experiment. The system was designed to capture the expertise of just one man, Jack D. Myers, MD, 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 Harry E. Pople. Development INTERNIST-I is the successor of the DIALOG system. For ten years, INTERNIST-I was the centerpiece of a Pittsburgh course entitled “The Logic of Problem-Solving in Clinical Diagnosis.” In consultation with faculty experts, much responsibility for data entry and updating of the system fell to the fourth-year medical students enrolled in the course. These students encoded the findings of standard clinicopatholog ...
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Expert Systems
In artificial intelligence, 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 reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. The first expert systems were created in the 1970s and then proliferated in the 1980s. Expert systems were among the first truly successful forms of artificial intelligence (AI) software. An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities. History Early development Soon after the dawn of modern computers in the late 1940s and early 1950s, researchers started realizing the immense potential these machines had for modern society. One of ...
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