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Готові списки джерел за темами / Aided detection

Добірка наукової літератури з теми "Aided detection"

Автор: Grafiati

Опубліковано: 28 вересня 2022

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Статті в журналах з теми "Aided detection":

1

Azavedo, E. "Computer aided detection." European Journal of Cancer 38, no. 11 (March 2002): S39. http://dx.doi.org/10.1016/s0959-8049(02)80100-9.

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2

Zheng, Bin, Xingwei Wang, Dror Lederman, Jun Tan, and David Gur. "Computer-Aided Detection." Academic Radiology 17, no. 11 (November 2010): 1401–8. http://dx.doi.org/10.1016/j.acra.2010.06.009.

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3

Scotti, James V. "Computer Aided Near Earth Object Detection." Symposium - International Astronomical Union 160 (1994): 17–30. http://dx.doi.org/10.1017/s0074180900046428.

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The Spacewatch program at the University of Arizona has pioneered automatic methods of detecting Near Earth Objects. Our software presently includes three modes of object detection: automatic motion identification; automatic streak identification; and visual streak identification. For automatic motion detection at sidereal drift rates, the 4σ detection threshold is near magnitude V = 20.9 for nearly stellar asteroid images. The automatic streak detection is able to locate streaks whose peak signal is above ~4σ and whose length is longer than about 10 pixels. Some visually detected streaks have had peak signals near ~1σ.Between 1990 September 25 and 1993 June 30, 45 new Near Earth asteroids, two comets and two Centaur's have been discovered with the system. An additional six comets, five Near Earth asteroids, and one Centaur were also “re-discovered”. The system has directly detected for the first time Near Earth Objects in the complete size range from about 5 kilometers to about 5 meters. Each month ~2,000 main belt asteroids are also detected.Future upgrades in both hardware, software, and telescope aperture may allow an order of magnitude increase in the rate of discovery of Near Earth Objects in the next several years. Several of the techniques proposed for the Spaceguard Survey have already been tested by Spacewatch, and others will need to be tested in the near future before such a survey can be implemented.

4

Raj, Abhishek, Alankrita, Akansha Srivastava, and Vikrant Bhateja. "Computer Aided Detection of Brain Tumor in Magnetic Resonance Images." International Journal of Engineering and Technology 3, no. 5 (2011): 523–32. http://dx.doi.org/10.7763/ijet.2011.v3.280.

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5

Zheng, Bin, Ratan Shah, Luisa Wallace, Christiane Hakim, Marie A. Ganott, and David Gur. "Computer-Aided Detection in Mammography." Academic Radiology 9, no. 11 (November 2002): 1245–50. http://dx.doi.org/10.1016/s1076-6332(03)80557-3.

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6

Basha, C. M. A. K. Zeelan, Maruthi Padmaja, and G. N. Balaji. "Computer Aided Fracture Detection System." Journal of Medical Imaging and Health Informatics 8, no. 3 (March 1, 2018): 526–31. http://dx.doi.org/10.1166/jmihi.2018.2324.

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7

Jun Yoon, Hong, Bin Zheng, Berkman Sahiner, and Dev P. Chakraborty. "Evaluating computer-aided detection algorithms." Medical Physics 34, no. 6Part1 (May 11, 2007): 2024–38. http://dx.doi.org/10.1118/1.2736289.

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8

Fenton, Joshua J., Christoph I. Lee, Guibo Xing, Laura-Mae Baldwin, and Joann G. Elmore. "Computer-Aided Detection in Mammography." JAMA Internal Medicine 174, no. 12 (December 1, 2014): 2032. http://dx.doi.org/10.1001/jamainternmed.2014.5410.

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9

Karssemeijer, N. "Computer-Aided Detection in Mammography." Imaging Decisions MRI 12, no. 3 (September 2008): 23–28. http://dx.doi.org/10.1111/j.1617-0830.2009.00130.x.

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10

Zrnec, Aljaž, and Dejan Lavbič. "Social network aided plagiarism detection." British Journal of Educational Technology 48, no. 1 (August 17, 2015): 113–28. http://dx.doi.org/10.1111/bjet.12345.

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Дисертації з теми "Aided detection":

1

曾偉明 and Wai-ming Peter Tsang. "Computer aided ultrasonic flaw detection and characterization." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31231007.

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2

Tsang, Wai-ming Peter. "Computer aided ultrasonic flaw detection and characterization /." [Hong Kong : University of Hong Kong], 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12344928.

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3

Donnelley, Martin, and martin donnelley@gmail com. "Computer Aided Long-Bone Segmentation and Fracture Detection." Flinders University. Engineering, 2008. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20080115.222927.

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Medical imaging has advanced at a tremendous rate since x-rays were discovered in 1895. Today, x-ray machines produce extremely high-quality images for radiologists to interpret. However, the methods of interpretation have only recently begun to be augmented by advances in computer technology. Computer aided diagnosis (CAD) systems that guide healthcare professionals to making the correct diagnosis are slowly becoming more prevalent throughout the medical field. Bone fractures are a relatively common occurrence. In most developed countries the number of fractures associated with age-related bone loss is increasing rapidly. Regardless of the treating physician's level of experience, accurate detection and evaluation of musculoskeletal trauma is often problematic. Each year, the presence of many fractures is missed during x-ray diagnosis. For a trauma patient, a mis-diagnosis can lead to ineffective patient management, increased dissatisfaction, and expensive litigation. As a result, detection of long-bone fractures is an important orthopaedic and radiologic problem, and it is proposed that a novel CAD system could help lower the miss rate. This thesis examines the development of such a system, for the detection of long-bone fractures. A number of image processing software algorithms useful for automating the fracture detection process have been created. The first algorithm is a non-linear scale-space smoothing technique that allows edge information to be extracted from the x-ray image. The degree of smoothing is controlled by the scale parameter, and allows the amount of image detail that should be retained to be adjusted for each stage of the analysis. The result is demonstrated to be superior to the Canny edge detection algorithm. The second utilises the edge information to determine a set of parameters that approximate the shaft of the long-bone. This is achieved using a modified Hough Transform, and specially designed peak and line endpoint detectors. The third stage uses the shaft approximation data to locate the bone centre-lines and then perform diaphysis segmentation to separate the diaphysis from the epiphyses. Two segmentation algorithms are presented and one is shown to not only produce better results, but also be suitable for application to all long-bone images. The final stage applies a gradient based fracture detection algorithm to the segmented regions. This algorithm utilises a tool called the gradient composite measure to identify abnormal regions, including fractures, within the image. These regions are then identified and highlighted if they are deemed to be part of a fracture. A database of fracture images from trauma patients was collected from the emergency department at the Flinders Medical Centre. From this complete set of images, a development set and test set were created. Experiments on the test set show that diaphysis segmentation and fracture detection are both performed with an accuracy of 83%. Therefore these tools can consistently identify the boundaries between the bone segments, and then accurately highlight midshaft long-bone fractures within the marked diaphysis. Two of the algorithms---the non-linear smoothing and Hough Transform---are relatively slow to compute. Methods of decreasing the diagnosis time were investigated, and a set of parallelised algorithms were designed. These algorithms significantly reduced the total calculation time, making use of the algorithm much more feasible. The thesis concludes with an outline of future research and proposed techniques that---along with the methods and results presented---will improve CAD systems for fracture detection, resulting in more accurate diagnosis of fractures, and a reduction of the fracture miss rate.

4

Bornefalk, Hans. "Computer-aided detection and novel mammography imaging techniques." Doctoral thesis, Stockholm, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3861.

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5

Kihlberg, Johan, and Simon Tegelid. "Map Aided Indoor Positioning." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-77766.

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The popularity of wireless sensor networks is constantly increasing, both for use instatic machine to machine environments as well as dynamic environments wherethe sensor nodes are carried by humans. Higher demands are put on real-timetracking algorithms of the sensor nodes, both in terms of accuracy and speed. This thesis addresses the issue of tracking persons wearing small sensor nodeswithin a radio network. Focus lies on fusing sensor data in an efficient way withconsideration to the computationally constrained sensor nodes. Different sensorsare stochastically modelled, evaluated, and fused to form an estimate of the person’sposition. The central approach to solve the problem is to use a dead reckoning methodby detecting steps taken by the wearer combined with an Inertial MeasurementUnit to calculate the heading of the person wearing the sensor node. To decreasethe unavoidable drift which is associated with a dead reckoning algorithm, a mapis successfully fused with the dead reckoning algorithm. The information from themap can to a large extent remove drift. The developed system can successfully track a person wearing a sensor nodein an office environment across multiple floors. This is done with only minorknowledge about the initial conditions for the user. The system can recover fromdivergence situations which increases the long term reliability.
Intresset för trådlösa sensornätverk ökar konstant, såväl för statiska maskintill-maskintillämpningar som för dynamiska miljöer där sensornoderna är burnaav människor. Allt högre krav ställs på positioneringsalgoritmer för sensornätverken,där både hög precision och låg beräkningstid ofta är krav. Denna rapport behandlar problemet med att bestämma positionen av personburnasensornoder. Rapportens fokus är att effektivt kombinera sensordatamed hänsyn till sensornodernas begränsade beräkningskapacitet. Olika sensorermodelleras stokastiskt, utvärderas och kombineras för att forma en skattning avsensornodens position. Den huvudsakliga metoden för att lösa problemet är att dödräkna sensornodbärarenssteg kombinerat med kompass och tröghetssensorer för att skattastegets riktning. En karta över byggnaden används för att reducera den annarsoundvikliga drift som härrör från dödräkning. Informationen från kartan visarsig i stor utsträckning kunna reducera den här driften. Det utvecklade systemet kan följa en person genom en kontorsmiljö somsträcker sig över flera våningsplan. Detta med enbart lite information om personensinitiala position. Systemet kan även återhämta sig från situationer däralgoritmen divergerar vilket ökar systemets pålitlighet på lång sikt.

6

Pons, Rodríguez Gerard. "Computer-aided lesion detection and segmentation on breast ultrasound." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/129453.

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This thesis deals with the detection, segmentation and classification of lesions on sonography. The contribution of the thesis is the development of a new Computer-Aided Diagnosis (CAD) framework capable of detecting, segmenting, and classifying breast abnormalities on sonography automatically. Firstly, an adaption of a generic object detection method, Deformable Part Models (DPM), to detect lesions in sonography is proposed. The method uses a machine learning technique to learn a model based on Histogram of Oriented Gradients (HOG). This method is also used to detect cancer lesions directly, simplifying the traditional cancer detection pipeline. Secondly, different initialization proposals by means of reducing the human interaction in a lesion segmentation algorithm based on Markov Random Field (MRF)-Maximum A Posteriori (MAP) framework is presented. Furthermore, an analysis of the influence of lesion type in the segmentation results is performed. Finally, the inclusion of elastography information in this segmentation framework is proposed, by means of modifying the algorithm to incorporate a bivariant formulation. The proposed methods in the different stages of the CAD framework are assessed using different datasets, and comparing the results with the most relevant methods in the state-of-the-art
Aquesta tesi es centra en la detecció, segmentació i classificació de lesions en imatges d'ecografia. La contribució d'aquesta tesi és el desenvolupament d'una nova eina de Diagnòstic Assistit per Ordinador (DAO) capaç de detectar, segmentar i classificar automàticament lesions en imatges d'ecografia de mama. Inicialment, s'ha proposat l'adaptació del mètode genèric de detecció d'objectes Deformable Part Models (DPM) per detectar lesions en imatges d'ecografia. Aquest mètode utilitza tècniques d'aprenentatge automàtic per generar un model basat en l'Histograma de Gradients Orientats. Aquest mètode també és utilitzat per detectar lesions malignes directament, simplificant així l'estratègia tradicional. A continuació, s'han realitzat diferents propostes d'inicialització en un mètode de segmentació basat en Markov Random Field (MRF)-Maximum A Posteriori (MAP) per tal de reduir la interacció amb l'usuari. Per avaluar aquesta proposta, s'ha realitzat un estudi sobre la influència del tipus de lesió en els resultats aconseguits. Finalment, s'ha proposat la inclusió d'elastografia en aquesta estratègia de segmentació. Els mètodes proposats per a cada etapa de l'eina DAO han estat avaluats fent servir bases de dades diferents, comparant els resultats obtinguts amb els resultats dels mètodes més importants de l'estat de l'art

7

Rabbani, Seyedeh Parisa. "Effect of image variation on computer aided detection systems." Thesis, KTH, Skolan för teknik och hälsa (STH), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123546.

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Computer Aided Detection (CAD) systems are expecting to gain significant importance in terms of reducing the work load of radiologists and enabling the large screening programs. A large share of CAD systems are based on learning from examples, to enables the decision making between the images with or without disease. Images are simplified to numerical descriptors (features vectors) and the system is trained with these features. The common practical problem with CAD systems is training the system with a data from a specific source and testing it on a data from a different source; the variations between sources usually affect the CAD system function. The possible solutions for this problem are (1) normalizing images to make them look more equal, (2) choosing less variation sensitive features and (3) modifying the classifier so that it classifies the data from different sources more accurately. In this project the effect of image variations on the developed CAD system on chest radio graphs for Tuberculosis is studied at Diagnostic Image Analysis Group. Tuberculosis is one of the major healthcare problems in some parts of the world (1.3 million deaths in 2007) [1]. Although the system has a great performance on the train and test data from the same source, using different sub dataset for training and testing the system does not lead to the same result. To limit the effect of image variation of the CAD systems three different approaches are applied for normalizing the images: (1) Simple normalization, (2) local normalization and (3) multi band local normalization. All three approaches enhance the performance of the system in case of various sub datasets for training and testing purposes. According to the improvement achieved by applying normalization it is suggested as a solution for the stated problem above. Although the outcome of this study has satisfactory result, there is always room for further investigations and studies; in specific testing different approaches for finding less variation sensitive features and modifying the classification procedure to a more variation tolerant process.

8

Llaquet, Bayo Antai. "Computer aided renal calculi detection using Convolutional Neural Networks." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-52254.

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In this thesis a novel approach is developed to detect urethral stones based on a computer-aided process. The input data is a CT scan from the patient, which is a high-resolution 3D grayscale image. The algorithm developed extracts the regions that might be stones, based on the intensity values of the pixels in the CT scan. This process includes a binarizing process of the image, finding the connected components of the resulting binary image and calculating the centroid of each of the components selected. The regions that are suspected to be stones are used as input of a CNN, a modified version of an ANN, so they can be classified as stone or non-stone. The parameters of the CNN have been chosen based on an exhaustive hyperparameter search with different configurations to select the one that gives the best performance. The results have been satisfactory, obtaining an accuracy of 98,3%, a sensitivity of 99,5% and a F1 score of 98,3%.

9

Guo, Yanhui. "Computer-Aided Detection of Breast Cancer Using Ultrasound Images." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/635.

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Ultrasound imaging suffers from severe speckle noise. We propose a novel approach for speckle reduction using 2D homogeneity and directional average filters to remove speckle noise. We transform speckle noise into additive noise using a logarithm transformation. Texture information is employed to describe the speckle characteristics of the image. The homogeneity value is defined using texture information value, and the ultrasound image is transformed into a homogeneity domain from the gray domain. If the homogeneity value is high, the region is homogenous and has less speckle noise. Otherwise, the region is nonhomogenous, and speckle noise occurs. The threshold value is employed to distinguish homogenous regions from regions with speckle noise obtained from a 2D homogeneity histogram according to the maximal entropy principle. A new directional filtering is convoluted to remove noise from pixels in a nonhomogenous region. The filtering processing iterates until the breast ultrasound image is homogenous enough. Experiments show the proposed method improves denoising and edge-preserving capability. We present a novel enhancement algorithm based on fuzzy logic to enhance the fine details of ultrasound image features, while avoiding noise amplification and over-enhancement. We take into account both the fuzzy nature of an ultrasound and feature regions on images, which are significant in diagnosis. The maximal entropy principle utilizes the gray-level information to map the image into fuzzy domain. Edge and textural information is extracted in fuzzy domain to describe the features of lesions. The contrast ratio is computed and modified by the local information. Finally, the defuzzification operation transforms the enhanced ultrasound images back to the spatial domain. Experimental results confirm a high enhancement performance including fine details of lesions, without over- or under-enhancement. Identifying object boundaries in ultrasound images is a difficult task. We present a novel automatic segmentation algorithm based on characteristics of breast tissue and eliminating particle swarm optimization (EPSO) clustering analysis, thus transforming the segmentation problem into clustering analysis. Mammary gland characteristics in ultrasound images are utilized, and a step-down threshold technique is employed to locate the mammary gland area. Experimental results demonstrate that the proposed approach increases clustering speed and segments the mass from tissue background with high accuracy.

10

Agarwal, Richa. "Computer aided detection for breast lesion in ultrasound and mammography." Doctoral thesis, Universitat de Girona, 2019. http://hdl.handle.net/10803/670295.

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In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM). Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis. Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging. In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used. The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screening
En el camp de les imatges de càncer de mama, els sistemes tradicionals de detecció assistida per ordinador (de l’anglès CAD) es van dissenyar utilitzant recursos informàtics limitats i pel·lícules de mamografia escanejades (del angles SFM) de qualitat d’imatge deficient, fet que va resultar en aplicacions poc robustes. Actualment, amb els avanços de les tecnologies, és possible realitzar imatges mèdiques en 3D i adquirir mamografies digitals (de l’anglès FFDM) d’alta qualitat. L’ultrasò automàtic de la mama (de l’anglès ABUS) ha estat proposat per adquirir imatges 3D de la mama amb escassa dependència del operador. Quan s’utilitza ABUS, la segmentació i seguiment de les lesions en el temps s ́on tasques complicades ja que la naturalesa 3D de les imatges fa que l’anàlisi sigui difícil i feixuc per els radiòlegs. Un dels objectius d’aquesta tesi és desenvolupar un marc per la segmentació semi-automàtica de lesions mamàries en volums ABUS. El volum de lesió 3D, en combinació amb l’anàlisi de la textura i el contorn, podria proporcionar informació valuosa per realitzar el diagnòstic radiològic. Tot i que els volums de ABUS són de gran interès, la mamografia de raigs X continua essent la modalitat d’imatge estàndard utilitzada per la detecció precoç del càncer de mama, degut principalment a la seva ràpida adquisició i rendibilitat. A més, amb l’arribada dels mètodes d’aprenentatge profund basats en xarxes neuronals convolucionals (de l’anglès CNN), els sistemes CAD moderns poden aprendre automàticament quines característiques de la imatge són més rellevants per realitzar un diagnòstic, fet que augmenta la utilitat d’aquests sistemes. Una de les limitacions de les CNN és que requereixen de grans conjunts de dades per entrenar, els quals són molt limitats en el camp de la imatge mèdica. En aquesta tesi, el tema de la poca disponibilitat d’imatges mediques s’aborda mitjançant dues estratègies: (i) utilitzant regions de la imatge com a entrada en comptes de les imatges de mida original, i (ii) mitjançant tècniques d’aprenentatge per transferència, en el que el coneixement après per a una determinada tasca es transfereix a una altra tasca relacionada (també conegut com a adaptació de domini). En primer lloc, la CNN entrenada en un conjunt de dades molt gran d’imatges naturals és adaptada per classificar regions de la imatge en tumor i no tumor de SFM i, en segon lloc, la CNN entrenada és adaptada per detectar tumors en FFDM. També s’ha investigat l’aprenentatge per transferència entre imatges naturals i FFDM. S’han utilitzat dos conjunts de dades públiques (CBIS-DDSM i INbreast) per aquest propòsit. En la fase final de la investigació, es proposa un marc de detecció automàtica de tumors utilitzant la mamografia original com entrada (en lloc de regions de la imatge) i que proporciona la localització de la lesió dins d’aquesta mamografia com a sortida. Per aquest propòsit s’utilitza una altra base de dades (OMI-DB). Els resultats obtinguts com a part d’aquesta tesi mostren millors rendiments en comparació amb l’estat de l’art, el que indica que els mètodes i marcs proposats tenen el potencial de ser implementats dins de sistemes CAD avançats, que poden ser utilitzats per radiòlegs en el cribratge del càncer de mama

Більше джерел

Книги з теми "Aided detection":

1

Coderre, David G. Computer Aided Fraud Prevention and Detection. New York: John Wiley & Sons, Ltd., 2009.

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2

Coderre, David, ed. Computer-Aided Fraud Prevention and Detection. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119203971.

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3

Agaian, S. S., and Jinshan Tang. Computer-aided cancer detection and diagnosis: Recent advances. Bellingham, Washington: SPIE Press, 2014.

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4

Natke, Hans Günther. Model-Aided Diagnosis of Mechanical Systems: Fundamentals, Detection, Localization, Assessment. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997.

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5

Natke, H. G. Model-aided diagnosis of mechanical systems: Fundamentals, detection, localization, and assessment. Berlin: Springer Verlag, 1997.

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6

Coderre, David G. Computer-aided fraud prevention and detection: A step-by-step guide. Hoboken, N.J: Wiley & Sons, 2009.

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7

Banik, Shantanu, Rangaraj M. Rangayyan, and J. E. Leo Desautels. Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-01656-1.

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8

International Conference on Industrial Electronics, Control, and Instrumentation (13th 1987 Cambridge, Mass.). IECON '87: Supplement : 1987 International Conference on Industrial Electronics, Control, and Instrumentation, 2-6 November 1987, Cambridge, Mass. [Bellingham, Wash., USA: SPIE--the International Society for Optical Engineering, 1987.

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9

International, Conference on Industrial Electronics Control and Instrumentation (13th 1987 Cambridge Mass ). IECON '87: Automated design and manufacturing : 1987 International Conference on Industrial Electronics, Control, and Instrumentation, 5-6 November 1987, Cambridge, Massachusetts. Bellingham, Wash., USA: SPIE--the Society of Photo-optical Instrumentation Engineers, 1987.

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10

International Conference on Industrial Electronics, Control, and Instrumentation (13th 1987 Cambridge, Mass.). IECON '87: Small computer applications, hardware and software : 1987 International Conference on Industrial Electronics, Control, and Instrumentation, 3 November 1987, Cambridge, Massachusetts. Edited by Gold Phillip, IEEE Industrial Electronics Society, Keisoku Jidō Seigyo Gakkai (Japan), and Society of Photo-optical Instrumentation Engineers. Bellingham, Wash., USA: SPIE--the Society of Photo-optical Instrumentation Engineers, 1987.

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Більше джерел

Частини книг з теми "Aided detection":

1

Hao, Chengpeng, Danilo Orlando, Jun Liu, and Chaoran Yin. "Knowledge-Aided Detectors." In Advances in Adaptive Radar Detection and Range Estimation, 45–102. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6399-4_3.

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2

Olivain, Julien, and Jean Goubault-Larrecq. "The Orchids Intrusion Detection Tool." In Computer Aided Verification, 286–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11513988_28.

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3

Wolper, Pierre, and Denis Leroy. "Reliable hashing without collision detection." In Computer Aided Verification, 59–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56922-7_6.

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4

Lennon, Áine M., and Wolfgang Buchalla. "Fluorescence-Aided Caries Excavation: FACE." In Detection and Assessment of Dental Caries, 99–106. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16967-1_10.

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5

Dörpinghaus, Meik. "Iterative Code-Aided Synchronized Detection." In On the Achievable Rate of Stationary Fading Channels, 101–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19780-2_6.

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6

Yoshida, H. "The Future: Computer-Aided Detection." In Virtual Colonoscopy, 175–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-79886-6_14.

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Nishikawa, Robert M. "Computer-aided Detection and Diagnosis." In Digital Mammography, 85–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-78450-0_6.

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8

Hao, Chengpeng, Danilo Orlando, Jun Liu, and Chaoran Yin. "Knowledge-Aided Localization Detectors." In Advances in Adaptive Radar Detection and Range Estimation, 155–92. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6399-4_5.

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9

Arlt, Stephan, and Martin Schäf. "Joogie: Infeasible Code Detection for Java." In Computer Aided Verification, 767–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31424-7_62.

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10

Kaiser, Alexander, Daniel Kroening, and Thomas Wahl. "Dynamic Cutoff Detection in Parameterized Concurrent Programs." In Computer Aided Verification, 645–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14295-6_55.

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Тези доповідей конференцій з теми "Aided detection":

1

Ren, Yinhao, Rui Hou, Dehan Kong, Lars J. Grimm, Jeffrey R. Marks, Joseph Y. Lo, and Yue Geng. "Multiview mammographic mass detection based on a single shot detection system." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2513136.

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2

Moradi, Mehdi, KenC L. Wong, Alexandros Karargyris, and Tanveer Syeda-Mahmood. "Quality controlled segmentation to aid disease detection." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Maciej A. Mazurowski. SPIE, 2020. http://dx.doi.org/10.1117/12.2549426.

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3

Doyle, Shannon, Francesco Dal Canton, Jelle Wesseling, Clara I. Sánchez, and Jonas Teuwen. "Mammary duct detection using self-supervised encoders." In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin, Karen Drukker, Maciej A. Mazurowski, Hongbing Lu, Chisako Muramatsu, and Ravi K. Samala. SPIE, 2022. http://dx.doi.org/10.1117/12.2612838.

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4

Wang, Weiyao, Aniruddha Tamhane, John Rzasa, James Clark, Therese Canares, and Mathias Unberath. "Otoscopy video screening with deep anomaly detection." In Computer-Aided Diagnosis, edited by Karen Drukker and Maciej A. Mazurowski. SPIE, 2021. http://dx.doi.org/10.1117/12.2581902.

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5

Eksi, Ziya, Emre Dandil, and Murat Cakiroglu. "Computer aided bone fracture detection." In 2012 20th Signal Processing and Communications Applications Conference (SIU). IEEE, 2012. http://dx.doi.org/10.1109/siu.2012.6204644.

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6

Capraro, Christopher T., Gerard T. Capraro, and Michael C. Wicks. "Knowledge Aided Detection and Tracking." In 2007 IEEE Radar Conference. IEEE, 2007. http://dx.doi.org/10.1109/radar.2007.374241.

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7

Hou, Rui, Yinhao Ren, Lars J. Grimm, Maciej A. Mazurowski, Jeffrey R. Marks, Lorraine King, Carlo C. Maley, Shelley Hwang, and Joseph Y. Lo. "Malignant microcalcification clusters detection using unsupervised deep autoencoders." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512829.

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8

Mathai, Tejas Sudharshan, Sungwon Lee, Daniel C. Elton, Thomas C. Shen, Yifan Peng, Zhiyong Lu, and Ronald M. Summers. "Lymph node detection in T2 MRI with transformers." In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin, Karen Drukker, Maciej A. Mazurowski, Hongbing Lu, Chisako Muramatsu, and Ravi K. Samala. SPIE, 2022. http://dx.doi.org/10.1117/12.2613273.

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9

Han, Yipeng, Mengjun Tao, and Xiaolu Zheng. "Ensembling learning for automated detection of diabetic retinopathy." In Computer-Aided Diagnosis, edited by Karen Drukker and Maciej A. Mazurowski. SPIE, 2021. http://dx.doi.org/10.1117/12.2582029.

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10

Liu, Bao, Ke-dong Wang, and Chao Zhang. "Star pattern recognition algorithm aided by inertial information." In International Symposium on Photoelectronic Detection and Imaging 2011, edited by John C. Zarnecki, Carl A. Nardell, Rong Shu, Jianfeng Yang, and Yunhua Zhang. SPIE, 2011. http://dx.doi.org/10.1117/12.902899.

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Звіти організацій з теми "Aided detection":

1

Kinnard, Lisa M. Computer-Aided Detection of Mammographic Masses in Dense Breast Images. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada437718.

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2

Lau, Beverly. Optimization of Breast Tomosynthesis Imaging Systems for Computer-Aided Detection. Fort Belvoir, VA: Defense Technical Information Center, May 2011. http://dx.doi.org/10.21236/ada545786.

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3

Doi, Kunio. Demonstration Project on Mammographic Computer-Aided Diagnosis for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada383367.

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4

Doi, Kunio. Demonstration Project on Mammographic Computer-Aided Diagnosis for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada405344.

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5

Doi, Kunio. Demonstration Project on Mammographic Computer-Aided Diagnosis for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada411233.

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6

Doi, Kunio. Demonstration Project on Mammographic Computer-Aided Diagnosis for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada359212.

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7

Hadjiiski, Lubomir. Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada484489.

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8

Hadjiiski, Lubomir M. Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, July 2005. http://dx.doi.org/10.21236/ada443710.

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9

Hadjiiski, Lubomir. Computer-Aided Interval Change Analysis of Microcalifications on Management for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, July 2006. http://dx.doi.org/10.21236/ada457664.

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10

Hadjiiski, Lubomir. Computer-Aided Interval Change Analysis of Microcalcifications on Mammograms for Breast Cancer Detection. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada433041.

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