Relevant bibliographies by topics / Computer Aided Diagnosis

Academic literature on the topic 'Computer Aided Diagnosis'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Computer Aided Diagnosis"

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Gilbert, F. J., and H. Lemke. "Computer-aided diagnosis." British Journal of Radiology 78, suppl_1 (January 2005): S1—S2. http://dx.doi.org/10.1259/bjr/23717382.

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van Ginneken, B. "Computer-Aided Diagnosis in Thoracic Computed Tomography." Imaging Decisions MRI 12, no. 3 (September 2008): 11–22. http://dx.doi.org/10.1111/j.1617-0830.2009.00129.x.

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Takeo, Hideya. "CAD (Computer Aided Diagnosis)." Journal of The Institute of Image Information and Television Engineers 63, no. 2 (2009): 191–93. http://dx.doi.org/10.3169/itej.63.191.

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Burroni, Marco, Rosamaria Corona, Giordana Dell’Eva, Francesco Sera, Riccardo Bono, Pietro Puddu, Roberto Perotti, Franco Nobile, Lucio Andreassi, and Pietro Rubegni. "Melanoma Computer-Aided Diagnosis." Clinical Cancer Research 10, no. 6 (March 15, 2004): 1881–86. http://dx.doi.org/10.1158/1078-0432.ccr-03-0039.

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Elbaum, Marek. "Computer-aided melanoma diagnosis." Dermatologic Clinics 20, no. 4 (October 2002): 735–47. http://dx.doi.org/10.1016/s0733-8635(02)00040-2.

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Mori, Yuichi, Shin-ei Kudo, Tyler Berzin, Masashi Misawa, and Kenichi Takeda. "Computer-aided diagnosis for colonoscopy." Endoscopy 49, no. 08 (May 24, 2017): 813–19. http://dx.doi.org/10.1055/s-0043-109430.

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Hassan, Mohamed Omer Khider, and Kasim M. Al-Hity. "Computer-Aided Diagnosis of Schistosomiasis." Journal of Clinical Engineering 37, no. 1 (2012): 29–34. http://dx.doi.org/10.1097/jce.0b013e31823fda36.

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Bainbridge, D. I. "Computer-Aided Diagnosis and Negligence." Medicine, Science and the Law 31, no. 2 (April 1991): 127–36. http://dx.doi.org/10.1177/002580249103100208.

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Aksungur, Varol Lütfü, Selma Sönmezoğlu Maraklı, Ayşe Akman, and Seydo Homan. "Computer-Aided Diagnosis of Genodermatoses." Journal of Dermatology 31, no. 2 (February 2004): 86–93. http://dx.doi.org/10.1111/j.1346-8138.2004.tb00513.x.

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Hall, Per N., Ela C. Claridge, and Jonathan D. Morris Smith. "Computer aided diagnosis of melanoma." Melanoma Research 5, Supplement 1 (May 1995): 19. http://dx.doi.org/10.1097/00008390-199505001-00024.

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Dissertations / Theses on the topic "Computer Aided Diagnosis"

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Elter, Matthias. "Computer-aided diagnosis of breast cancer." Tönning Lübeck Marburg Der Andere-Verl, 2010. http://d-nb.info/1001110773/04.

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Mori, Kensaku. "Advances in Computer Aided Diagnosis and Computer Assisted Surgery." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10452.

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Nakamura, Yoshihiko, Takayuki Kitasaka, Kensaku Mori, and Yasuhito Suenaga. "COMPUTER AIDED DIAGNOSIS FOR ABDOMINAL SURGICAL PLANNING." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10470.

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Malone, John Philip. "Computer-aided diagnosis of diffuse lung disease." Thesis, University of Bristol, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440143.

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Tembey, Mugdha. "Computer-Aided Diagnosis for Mammographic Microcalcification Clusters." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000168.

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PUTZU, LORENZO. "Computer aided diagnosis algorithms for digital microscopy." Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266877.

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Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today.
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Feudjio, Kougoum Cyrille Désiré. "Segmentation of mammographic images for computer aided diagnosis." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10152/document.

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Les outils d’aide au diagnostic sont de nos jours au cœur de plusieurs protocoles cliniques car ils améliorent la qualité du diagnostic posé et des soins médicaux. Ce travail de recherche met en avant une architecture hiérarchique pour la conception d'un outil d'aide à la détection du cancer du sein robuste et performant. Il s’intéresse à la réduction des fausses alarmes en identifiant les régions potentiellement cancérogènes. La gamme dynamique des niveaux de gris des zones sombres est étirée pour améliorer le contraste entre la région du sein et l'arrière plan et permettre une meilleure extraction de celle-ci. Toutefois, le muscle pectoral demeure incrusté dans la région du sein et interfère avec l'analyse des tissus. Son extraction est à la fois difficile et complexe à mettre en œuvre à cause de son chevauchement avec les tissus denses du sein. Dans ces conditions, même en exploitant l'information spatiale pendant la clusterisation par un algorithme de fuzzy C-means ne produit pas toujours des résultats de segmentation pertinents. Pour s'affranchir de cette difficulté, une étape de validation suivie d'un ajustement de contour est mise sur pied pour détecter et corriger les imperfections de segmentation. La seconde étape est consacrée à la caractérisation de la densité des tissus. Pour faire face au problème de variabilité des distributions de niveaux de gris dans les classes de densités, nous introduisons une modification de contraste basée sur un transport optimisé de niveaux de gris. Grâce à cette technique, la surface relative de tissus denses estimée par simple segmentation est très fortement corrélée aux classes de densités issues d’un jeu de données étiquetées
Computer-aided diagnosis systems are currently at the heart of many clinical protocols since they significantly improve diagnosis making and therefore medical care. This research work therefore puts forward a hierarchical architecture for the design of a robust and efficient CAD tool for breast cancer detection. More precisely, it focuses on the reduction of false alarms rate through the identification of image regions of foremost interest i.e potential cancerous areas. The dynamic range of gray level intensities in dark regions is, first of all stretched to enhance the contrast between tissues and background and thus favors accurate breast region extraction. A second segmentation follows since pectoral muscle which regularly tampers breast tissue analysis remains inlaid in the foreground region. Extracting pectoral muscle tissues is both hard and challenging due to its overlap with dense tissues. In such conditions, even exploiting spatial information during the clustering process of the fuzzy C-means algorithm does not always produce a relevant segmentation. To overcome this difficulty, a new validation process followed by a refinement strategy is proposed to detect and correct the segmentation imperfections. The second macro-step is devoted to breast tissue density analysis. To address the variability in gray levels distributions with of mammographic density classes, we introduce an optimized gray level transport map for mammographic image contrast standardization. Thanks to this technique, dense region areas computed using simple thresholding are highly correlated to density classes from an annotated dataset
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GIANNINI, VALENTINA. "Computer Aided Diagnosis systems for MR cancer detection." Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2496445.

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The research activity conducted during my PhD aims to develop two different Computer Aided Diagnosis (CAD) systems for breast and prostate cancer diagnosis using Magnetic Resonance Imaging. During the first part of this thesis I will illustrate a fully automatic CAD system for breast cancer detection and diagnosis with Dynamic Contrast Enhanced MRI (DCE-MRI) developed by our group. The main goal of a CAD system is lesions detection and characterization. The processing pipeline includes automatic segmentation of the breast and axillary regions, registration of unenhanced and contrast-enhanced frames, lesion detection and classification according to kinetic and morphological criteria. During my PhD I, firstly, studied the physiological phenomena correlated to breast tumors growth and diagnosis, then I elaborated and created C++ algorithms for: 1. breasts segmentation, where the breasts and axillary regions are automatically identified in order to reduce the computational burden and preventing false positives (FP) due to enhancing structures (such as the heart and extra-breast vessels) which are not of clinical interest. 2. lesion detection, in which suspicious areas showing contrast enhancement are automatically segmented and FPs are identified and discarded. These step are innovative as they are fully automatic, thus they do not suffer of inter- and intra-operator variability, and because of the normalization process, based on the mammary arteries segmentation, that makes the system able to deal with images coming from different centers, thus having different acquisition parameters. The second part of my thesis will concern the development of a CAD system for prostate cancer. The importance of this project is associated to the recent interest in adapting focal methods of tissue ablation, such as cryotherapy and Focused Ultrasound guided by MR (MRgFUS), to cure or control localized prostate cancer. Focal treatments rely on imaging to locate tumor, to determine the staging of disease, to detect recurrences and to guide the treatment. The aim of this part of my PhD was to create a multispectral computer aided diagnosis system able to: a) detect the tumor in order to guide real-time biopsy, b) characterize the malignancy of the lesion and c) guide the local treatment, by adopting a new multispectral approach. In this project I, actively, elaborated and developed C++ algorithm to register different datasets and to monitor the focal treatment using Diffusion Weighted-MRI (DWI) self-made acquisitions.The registration between T2-w, DCE-MRI and DWI images are applied in order to correct for patients movements and DWI distortions. Results obtained within 19 patients showed a Dice’s overlap coefficient higher than 0.7, considered optimal in literature. Monitoring the focal therapy was the aim of the last part of this project, that I actively developed during a visiting period in the Radiological Science Laboratory of the Stanford University (Kim Butts Pauly Research Lab). The main goal was to characterize the role of the DWI during MRgFUS. DWI, in fact, is very sensitive to cell death and tissue damage and information can be used to evaluate the treatment without relocating the patient and the applicators and without involving the administration of contrast agent. In this study, I wanted to assess the use of DWI images to estimate prostate tissue damage during HIFU ablation, by measuring diffusion coefficients of canine prostate pre and post ablation, using multiple b-factors ranging up to 3500 s/mm2 . This study demonstrated a bi-exponential decay of the signal increasing the b-values suggesting the presence of two different type of diffusion, called fast and slow.
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Duchesne, Simon. "Computer aided diagnosis in temporal lobe epilepsy and Alzheimer's dementia." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100354.

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Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and use cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).
The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.
The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of voxel-based morphometry measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy. These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management.
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Zhou, Yu. "Computer Aided Diagnosis of Melanoma - A Photometric Stereo Based Approach." Thesis, University of the West of England, Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.524729.

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This thesis describes a new melanoma diagnosii:s framework based on photometric stereo - a linear and computationally efficient method for extracting 3D data. The primary goal of this thesis is to investigate texture analysis of skin, including 3D based techniques, to realize a relatively reliable and accurate diagnosis of melanoma. In this thesis, a novel semi-automatic segmentation algorithm based on the normalized cut is proposed at first to realize fast segmentatioID. of melanoma images. As classic normalized cut involves generalized eigenvalue decompositioru, it can be extremely slow in segmenting large skin images even if the results are usually very impressive. Here a hierarchical normalized cut is proposed to conduct fast segmentation of skim lesion images with final results as good as the classic normalized cut in most cases. In addition, a new border analysis framework called centroid distance diagram is formulated to describe the border irregularity of melanoma images. This Fourier transform based approach gives a series of border irregularity descriptors insensitive to tire scate and rotation of tire images. Descriptors generated by using this method are proved to be effective features in describing malignant melanomas. The relationship between classic convexity and centroid convexity of border curves is examined in detail and the non-centroid -convexity index is generated to measure the irregularity of border curves. Moreover, statistical principal curvature patterns of skin surfaces are formulated to describe 3D melanoma shapes. The principal curvatures of skin surfaces are extracted by using the normal vector data obtained from photometric stereo. A robust estimator of these curvature parameters is embedded in this algorithm by using Gaussian kernels. Finally, an ensemble classifier for melanoma diagnosis is formulated by combining classifiers designed with various 2D /3D descriptors, e.g., border irregularity descriptors, colour variation descriptors and 3D principal curvature pattern descriptors. Even when the performance of the classifier obtained from one single group of descriptors is relatively poor, the ensemble classifier can offer highly impressive performances. Two novel schemes for designing this ensemble I classifier are proposed and compared with other well-known ensemble classifier designing methods, including Boosting and majority voting. Experiment studies suggest that the method named Bayesian II gives the best mean sensitivity (91.08 percent) and the best consistency level of sensitivity while the mean specificity can achieve 90.89 percent. The present work thus makes a novel contribution to the existing methods for computer aided diagnosis of melanoma. However, there are still many intriguing directions for further research works such as enhancing the accuracy of 3D data, recognition of subtypes of melanoma, introducing other descriptors in diagnosis and so on.
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Books on the topic "Computer Aided Diagnosis"

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Elseid, Arwa Ahmed Gasm, and Alnazier Osman Mohammed Hamza. Computer-Aided Glaucoma Diagnosis System. First edition. | Boca Raton, FL : CRC Press, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780367406288.

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Lung imaging and computer aided diagnosis. Boca Raton, FL: CRC Press, 2012.

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Das, Rik, Sudarshan Nandy, and Siddhartha Bhattacharyya. Disruptive Trends in Computer Aided Diagnosis. New York: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003045816.

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Su, Ruidan, and Han Liu, eds. Medical Imaging and Computer-Aided Diagnosis. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5199-4.

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Hastie, Trevor. Computer-aided diagnosis of mammographic masses. [Toronto]: University of Toronto, Dept. of Statistics, 1996.

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Su, Ruidan, Yudong Zhang, Han Liu, and Alejandro F Frangi, eds. Medical Imaging and Computer-Aided Diagnosis. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-6775-6.

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Cook, Ian Ainsworth. Computer aided evaluation of early neoplasms. [New Haven: s.n.], 1987.

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Yin, Xiao-Xia, Sillas Hadjiloucas, and Yanchun Zhang. Pattern Classification of Medical Images: Computer Aided Diagnosis. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57027-3.

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

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Murugappan, M., and Yuvaraj Rajamanickam, eds. Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97845-7.

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Book chapters on the topic "Computer Aided Diagnosis"

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Gale, A. G., E. J. Roebuck, P. J. Riley, and B. S. Worthington. "Computer Aided Diagnosis in Mammography." In Computer Assisted Radiology / Computergestützte Radiologie, 427–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-52247-5_67.

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Ridderikhoff, J. "Obstacles in Computer-Aided Diagnosis." In Present Status of Computer Support in Ambulatory Care, 86–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-93355-4_12.

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Swett, Henry A. "Computer-Aided Diagnosis in Radiology." In Computer Assisted Radiology / Computergestützte Radiologie, 738–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-00807-2_118.

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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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Summers, Ronald M., and Hiroyuki Yoshida. "Future Directions: Computer-Aided Diagnosis." In Atlas of Virtual Colonoscopy, 55–62. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21558-7_8.

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Vitulano, S., C. Ruberto, and M. Nappi. "Computer aided diagnosis in radiology." In Lecture Notes in Computer Science, 520–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60697-1_154.

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Das, Rik, Siddhartha Bhattacharyya, and Sudarshan Nandy. "Evolution of Computer Aided Diagnosis." In Disruptive Trends in Computer Aided Diagnosis, 1–9. New York: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003045816-2.

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Suzuki, Kenji, and Abraham H. Dachman. "Computer-Aided Diagnosis in Computed Tomographic Colonography." In Atlas of Virtual Colonoscopy, 163–82. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5852-5_12.

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Rasse, Anne. "Error diagnosis in finite communicating systems." In Computer Aided Verification, 114–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55179-4_12.

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Peixoto, Hugo, and Victor Alves. "Computer-Aided Diagnosis in Brain Computed Tomography Screening." In Advances in Data Mining. Applications and Theoretical Aspects, 62–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03067-3_7.

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Conference papers on the topic "Computer Aided Diagnosis"

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Wang, Lili, Jie Wu, Guang Yang, and Bin Zheng. "Computer-aided staging of gastric cancer using radiomics signature on computed tomography imaging." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Maciej A. Mazurowski. SPIE, 2020. http://dx.doi.org/10.1117/12.2549667.

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Kim, Seong Tae, Hakmin Lee, Hak Gu Kim, and Yong Man Ro. "ICADx: interpretable computer aided diagnosis of breast masses." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2293570.

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Weissman, Claire, Lilly Roelofs, Jacob Furst, Daniela Stan Raicu, and Roselyne Tchoua. "Similarity-based uncertainty scores for computer-aided diagnosis." 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/10.1117/12.2611515.

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Berglin, Samuel, Eura Shin, Daniela Raicu, and Jacob Furst. "Efficient learning in computer-aided diagnosis through label propagation." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512803.

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Heidari, Morteza, Gopichandh Danala, Wei Qian, Yuchen Qiu, Bin Zheng, and Seyedeh-Nafiseh Mirnia-harikandehei. "Association of computer-aided detection results and breast cancer risk." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512585.

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Agrawal, Devanshu, Hong-Jun Yoon, Georgia Tourassi, and Jacob D. Hinkle. "Computer-aided detection using non-convolutional neural network Gaussian processes." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2513625.

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Danala, Gopichandh, Faranak Aghaei, Morteza Heidari, Teresa Wu, Bhavika Patel, and Bin Zheng. "Computer-aided classification of breast masses using contrast-enhanced digital mammograms." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2293136.

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Cha, Kenny H., Lubomir M. Hadjiiski, Heang-Ping Chan, Elaine M. Caoili, Richard H. Cohan, Alon Z. Weizer, Marshall N. Gordon, and Ravi K. Samala. "Computer-aided detection of bladder wall thickening in CT urography (CTU)." In Computer-Aided Diagnosis, edited by Kensaku Mori and Nicholas Petrick. SPIE, 2018. http://dx.doi.org/10.1117/12.2293844.

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Brown, James M., Vincent Andrearczyk, Veysi Yildiz, Deniz Erdogmus, Stratis Ioannidis, Michael F. Chiang, Jayashree Kalpathy-Cramer, Henning Müller, J. Peter Campbell, and Mara Graziani. "Improved interpretability for computer-aided severity assessment of retinopathy of prematurity." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Kensaku Mori. SPIE, 2019. http://dx.doi.org/10.1117/12.2512584.

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Scheeve, Thom, Nikoo Dehghani, Quirine E. W. van der Zander, Ayla Thijssen, Ramon-Michel Schreuder, Ad A. M. Masclee, Erik J. Schoon, Fons van der Sommen, and Peter H. N. de With. "How does image quality affect computer-aided diagnosis of colorectal polyps?" In Computer-Aided Diagnosis, edited by Khan M. Iftekharuddin and Weijie Chen. SPIE, 2023. http://dx.doi.org/10.1117/12.2654167.

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Reports on the topic "Computer Aided Diagnosis"

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Floyd, Carey E. Computer Aided Breast Cancer Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 1996. http://dx.doi.org/10.21236/ada325798.

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Floyd, Carey E. Computer Aided Breast Cancer Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada392958.

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Floyd, Carey E. Computer Aided Breast Cancer Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada383108.

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Jiang, Yulei. Computer-Aided Diagnosis of Digital Mammograms. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada396524.

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Jiang, Yulei. Computer-Aided Diagnosis of Digital Mammograms. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada431258.

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Jiang, Yulei. Computer-Aided Diagnosis of Breast Lesions. Fort Belvoir, VA: Defense Technical Information Center, June 2002. http://dx.doi.org/10.21236/ada410986.

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Jiang, Yulei. Computer-Aided Diagnosis of Digital Mammograms. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada421590.

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Kupinski, Matthew A. Investigation of Genetic Algorithms for Computer-Aided Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada393995.

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Chan, Heang P. Digital Mammography: Advanced Computer-Aided Breast Cancer Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada420159.

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Kupinski, Matthew A. Investigation of Genetic Algorithms for Computer-Aided Diagnosis. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada391457.

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