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Статті в журналах з теми "Glaucome – Diagnostic"
Bresson, H., X. Zanlonghi, S. Bodet, J. P. Moisan, and F. Gerson. "473 Le glaucome : bases moléculaires et diagnostic génétique en France." Journal Français d'Ophtalmologie 28 (March 2005): 282. http://dx.doi.org/10.1016/s0181-5512(05)73593-2.
Повний текст джерелаPerossini, M., M. Romagnoli, T. Perossini, M. Figus, S. Benedetti, L. Corucci, and M. Nardi. "279 Le diagnostic précoce du glaucome : comparaison entre différentes techniques." Journal Français d'Ophtalmologie 28 (March 2005): 228–29. http://dx.doi.org/10.1016/s0181-5512(05)74676-3.
Повний текст джерелаWroten, Chris. "Nouveautés et thérapies ´émergentes pour le glaucome." Canadian Journal of Optometry 80, no. 2 (June 1, 2018): 55–59. http://dx.doi.org/10.15353/cjo.80.271.
Повний текст джерелаBiton, C., J. P. Lemoal, J. P. Djoumegan, T. Khuc, and M. Bonsch. "510 Diagnostic échographique anténatal d’un glaucome congénital bilatéral : à propos d’un cas." Journal Français d'Ophtalmologie 28 (March 2005): 291. http://dx.doi.org/10.1016/s0181-5512(05)73630-5.
Повний текст джерелаPromelle, V., C. Iscar, M. Grenot, C. Gondry-Jouet, and S. Milazzo. "Les anomalies congénitales de la papille : un diagnostic différentiel du glaucome congénital." Journal Français d'Ophtalmologie 40, no. 1 (January 2017): e35-e36. http://dx.doi.org/10.1016/j.jfo.2016.08.014.
Повний текст джерелаBohere, E., N. Franquet, F. Delanoy, A. Scheffer, and G. Laverdure. "504 Intérêt de l’imagerie cérébrale dans le diagnostic de glaucome à pression normale." Journal Français d'Ophtalmologie 32 (April 2009): 1S155. http://dx.doi.org/10.1016/s0181-5512(09)73628-9.
Повний текст джерелаCuvier, Marion. "Examens complémentaires dans le cadre d’un diagnostic de glaucome : Mme L., une configuration particulière." Revue Francophone d'Orthoptie 6, no. 3 (July 2013): 114–18. http://dx.doi.org/10.1016/j.rfo.2013.08.001.
Повний текст джерелаAlmárcegui Lafita, C., J. Fernandez Tirado, B. Melcon Sanchez Friera, P. Uclés Moreno, JR Valdizán Usón, and FM Honrubia López. "Étude des composants de l'électrorétinogramme par inversion de damier dans le diagnostic précoce du glaucome." Neurophysiologie Clinique/Clinical Neurophysiology 27, no. 2 (April 1997): 109–15. http://dx.doi.org/10.1016/s0987-7053(97)85663-8.
Повний текст джерелаRixon, Andrew J., Rebecca A. Wenig, and Brittany N. Rigdon. "Un cas de glaucome à angle ouvert consécutif au syndrome de Posner-Schlossman." Canadian Journal of Optometry 81, no. 3 (September 4, 2019): 39–48. http://dx.doi.org/10.15353/cjo.v81i3.1561.
Повний текст джерелаGheck, L., P. Koskas, S. N’ghiem-Buffet, and G. Chaine. "523 Apports de l’échographie Doppler couleur orbitaire dans le diagnostic étiologique d’un glaucome néovasculaire : à propos d’un cas." Journal Français d'Ophtalmologie 28 (March 2005): 294. http://dx.doi.org/10.1016/s0181-5512(05)73643-3.
Повний текст джерелаДисертації з теми "Glaucome – Diagnostic"
Rami, Sabrina. "Application de la méthode de Delange au diagnostic précoce du glaucome /." [S.l.] : [s.n.], 1998. http://library.epfl.ch/theses/?nr=1853.
Повний текст джерелаSentenac-Mourou, Hélène. "Contribution au diagnostic précoce des hypertonies oculaires primitives par l'exploration de la fonction colorée." Bordeaux 2, 1992. http://www.theses.fr/1992BOR2M155.
Повний текст джерелаMvoulana, Amed. "Vers un ophtalmologiste "augmenté" : analyse d'images rétiniennes pour l'aide au diagnostic précoce du glaucome." Thesis, Université Gustave Eiffel, 2022. http://www.theses.fr/2022UEFL2007.
Повний текст джерелаOcular diseases are at the core of major public health issues. One of them, glaucoma, requires early screening to ensure effective treatment of affected patients, and prevent irreversible visual damages. The advent of so-called computer vision and deep learning approaches has led to a paradigm shift in the field of ophthalmology, providing unprecedented support in diagnostic and therapeutic choices. In this thesis, we propose new methods for the development of intelligent systems dedicated to the early detection of glaucoma from retinal images. In particular, we aim at deploying of mobile-based computer-aided diagnosis systems, for remote screening. Firstly, we proposed a method aiming at analyzing the optic nerve head, featured by morphological changes in the presence of glaucoma. Based on a precise algorithm for segmenting the structures of the optic disc and the cup within it, the method extracts clinically relevant measures such as the cup-to-disc ratio, the inferior-superior-nasal-temporal (ISNT) sectors and the neuroretinal rim area. A clinical protocol based on ophthalmic references is drawn to screen for glaucoma, and give indications about the stages of development of the neuropathy (early, moderate or advanced glaucoma). Although very accurate screening, with a performance rate of 94% on the evaluation base (DRISHTI-GS1), this method has highlighted the need to improve generalizability, particularly in the presence of glaucomatous nerve heads without excavation (false negatives) or large healthy nerve heads (false positives). Secondly, we proposed a method based on deep learning algorithms, allowing an automated interpretation of healthy or glaucomatous retinas. This work exploits state-of-the-art convolutional neural networks (VGG-16, ResNet50, Inception-v3, MobileNet and DenseNet121), and proposes an efficient transfer learning method to adapt these networks to the glaucoma screening. These models achieve an AUC of more than 0.97, however, this comparative study has identified the needs for developing efficient models for deployment in clinical conditions: 1) a consistent retinal image dataset in terms of size, inter-class balance, diagnostic reliability and clinical variability, 2) interpretable and explainable models, allowing specialists to understand and discuss the screening result.In this sense, we propose in a third step a method exploiting recent advances in semi-supervised learning, for the generation of synthetic retinal images. The proposed algorithm, BAGAN (for Balancing GAN), allows to produce from a reference image dataset (REFUGE), a new dataset filling the inter-class imbalance potentially responsible for diagnostic bias, while meeting the criteria of image quality and clinical diversity. We have demonstrated the relevance of such dataset in the further development of semi-supervised diagnosis algorithms. Finally, a brand new interface, available on desktop and mobile platforms, has been designed for ophthalmologists and health professionals. Smart and intuitive, it integrates various functionalities based on the developed algorithms, and allows real-time screening to contribute to the improvement of eye health care
ROULEAU, JACQUES. "Systeme expert applique au diagnostic et au traitement du glaucome primitif a angle ouvert." Clermont-Ferrand 1, 1989. http://www.theses.fr/1989CLF13803.
Повний текст джерелаBOUSSION, BERTRAND. "Le flicker test : une nouvelle methode de diagnostic precoce dans le glaucome primitif a angle ouvert." Angers, 1991. http://www.theses.fr/1991ANGE1086.
Повний текст джерелаSousa, Jefferson Alves de. "Diagnóstico de glaucoma em retinografias utilizando funções geoestatística." Universidade Federal do Maranhão, 2017. http://tedebc.ufma.br:8080/jspui/handle/tede/1536.
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Glaucoma is one of the diseases that mopstly causes blindness, according to estimates by the World Health Organization (WHO). The Brazilian Council of Ophthalmology (CBO) estimates that in Brazil there are 985 thousand glaucoma patients with more than 40 years of age. The use of computer aided detection and diagnosis systems (CAD - Computer Aided Detection and CADx - Computer Aided Diagnosis) has contributed to increase the chances of detection and correct diagnoses. They provide a second opinion, assisting the experts in making decisions about the treatment of glaucoma. The main objective of this work is to present a method for automatic diagnosis of glaucoma in retinography images using texture characteristics. The Local Binary Pattern is used to generate a representation of the texture patterns of the image. The geostatistical functions, semivariogram, semimagram, covariogram and correlogram, are used as texture extractors. With the generated characteristics, a step is made to select the best classification model using the genetic algorithm. Then sorting is performed using the Support Vector Machine. The best result was an accuracy of 91%, sensitivity of 95% and specificity of 88%, proving that the characteristics generated by the geostatistical functions for texture extraction generate a satisfactory discriminant set.
O glaucoma e uma das doenças que mais causam cegueira em todo o mundo segundo estimativa da Organização Mundial da Saúde (OMS). O Conselho Brasileiro de Oftalmologia (CBO) estima que no Brasil existam 985 mil portadores de glaucoma com mais de 40 anos de idade. A utilização de sistemas de detecção e diagnóstico auxiliados por computador (CAD - Computer Aided Detection e CADx -Computer Aided Diagnosis) tem contribuído para aumentar as chances de detecção e diagnósticos corretos, que proporcionam, uma opinião preliminar, auxiliando os especialistas na tomada de decisões sobre o tratamento do glaucoma. O principal objetivo deste trabalho e apresentar um método para diagnóstico automático de glaucoma em imagens de retinografia utilizando características de textura. Neste trabalho, o Local Binary Pattern é usado para gerar uma representação do padrões de textura da imagem. As funções geoestatísticas, semivariograma, semimadograma, covariograma e correlograma, sao utilizadas como extratores de textura. Com as características geradas e realizada uma etapa para selecionar o melhor modelo de classificação utilizando o algoritmo genético. Em seguida é realizada a classificação usando a Maquina de Vetores de Suporte. O método apresentou como melhor resultado uma acurácia de 91%, sensibilidade de 95% e especificidade de 88%, comprovando que as características geradas pelas funções geoestatísticas para extração de textura geram um conjunto discriminante satisfatório.
Pascal, Lucas. "Optimization of deep multi-task networks." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS535.
Повний текст джерелаMulti-task learning (MTL) is a learning paradigm involving the joint optimization of parameters with respect to multiple tasks. By learning multiple related tasks, a learner receives more complete and complementary information on the input domain from which the tasks are issued. This allows to gain better understanding of the domain by building a more accurate set of assumptions of it. However, in practice, the broader use of MTL is hindered by the lack of consistent performance gains observed by deep multi-task networks. It is often the case that deep MTL networks suffer from performance degradation caused by task interference. This thesis addresses the problem of task interference in Multi-Task learning, in order to improve the generalization capabilities of deep neural networks
Denniss, Jonathan. "Diagnostic imaging and the structure-function relationship in glaucoma." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/diagnostic-imaging-and-the-structurefunction-relationship-in-glaucoma(24b94e53-d0b9-4437-a639-8ea739049d22).html.
Повний текст джерелаLee, Simon. "Visual monitoring of glaucoma." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291080.
Повний текст джерелаSilva, Leopoldo Magacho dos Santos. "Avaliação de parametros estruturais no diagnostico do glaucoma." [s.n.], 2004. http://repositorio.unicamp.br/jspui/handle/REPOSIP/310035.
Повний текст джерелаTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciencias Medicas
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Resumo: Objetivo: Avaliar a Sensibilidade e Especificidade de parâmetros estruturais (topografia de disco óptico e análise da camada de fibras nervosas da retina) no diagnóstico do glaucoma. Métodos: Pacientes com glaucoma primário de ângulo aberto foram selecionados do ambulatório de glaucoma da Unicamp. Indivíduos normais foram recrutados junto a voluntários dentro da equipe médica, amigos e parentes dos pacientes. Os participantes, após aprovação do Comitê de Ética da instituição, foram submetidos a exame oftalmológico completo, campimetria computadorizada (Humphrey Full Threshold, programa 24-2), topografia de disco óptico com o TOPSS e análise da camada de fibras nervosas da retina por meio da polarimetria de varredura a laser (GDx, versão 1.0.12). Curvas ROC foram criadas para se estabelecer novos pontos de corte para cada parâmetro individual de ambos os instrumentos. Em seguida, a análise de regressão multivariada foi empregada com parâmetros da topografia de disco óptico e polarimetria de varredura a laser separados e em conjunto para obter uma melhor relação sensibilidade/especificidade (Se/Es) no diagnóstico do glaucoma. Resultados: Cento e doze indivíduos com glaucoma e 88 normais foram incluídos. Os parâmetros individuais da polarimetria de varredura a laser com melhor capacidade em diferenciar olhos normais de glaucomatosos foram: The Number (Se: 79,5%, Es: 81,8%, área abaixo da curva ROC - aROC: 0,870), Maximum Modulation (Se: 83,0%, Es: 76,1%, aROC: 0,842) e Ellipse Modulation (Se: 65,2%, Es: 88,6%, aROC: 0,831), enquanto Average Disc Diameter (Se: 64%, Sp: 89%, aROC: 0.824), Total Disc Area (Se: 85%, Sp: 66%, aROC: 0.802) e Cup Area (Se: 69%, Sp: 85%, aROC: 0.797) foram os melhores parâmetros individuais da topografia de disco óptico. Entretanto, a análise discriminante apresentou melhores resultados que os parâmetros individuais, tanto para a polarimetria de varredura a laser (Se: 87,5%, Es: 86,4%, aROC: 0,900), topografia de disco óptico (Se: 90%, Sp: 81%, aROC: 0,910), quanto para a combinação de parâmetros derivados de ambos os instrumentos (Se: 93%, Es: 91%, aROC: 0,970). Conclusão: Parâmetros estruturais, avaliados pela topografia de disco óptico e pela polarimetria de varredura a laser apresentam boa capacidade na diferenciação entre olhos normais e glaucomatosos. Entretanto, os melhores resultados são obtidos ao combiná-los em uma função linear discriminante com parâmetros derivados de ambos os instrumentos
Abstract: Purpose: To test the ability of structural parameters (as measured by the Scanning Laser Polarimetry and Confocal Scanning Laser Ophthalmoscopy) to discriminate between normal and glaucomatous eyes. Methods: After approval of the Ethics Committee of the University of Campinas, primary open-angle glaucoma patients were selected from the glaucoma service. Normal individuals were recruited from volunteers among the medical staff, university members, family and friends of patients. All individuals underwent a thorough ophthalmic evaluation, a 24-2 full threshold Humphrey visual field, Scanning Laser Polarimetry with the GDx and Confocal Scanning Laser Ophthalmoscopy with the TOPSS. Cut-off points were selected and ROC curves were created for each individual Confocal Scanning Laser Ophthalmoscopy and Scanning Laser Polarimetry parameters. Finally, multivariate discriminant formulas were developed in order to achieve a better sensitivity (Se)/specificity (Sp) ratio for the diagnosis of glaucoma, initially separately for each device, and then combining parameters from Confocal Scanning Laser Ophthalmoscopy and Scanning Laser Polarimetry. Results: One-hundred and twelve patients with primary open-angle glaucoma and 88 normal individuals were enrolled in the study. The best Scanning Laser Polarimetry parameters in differentiating normal individuals from glaucoma patients were: The Number (Se: 79,5%, Es: 81,8%, area under the ROC curve- aROC: 0,870), Maximum Modulation (Se: 83,0%, Es: 76,1%, aROC: 0,842) and Ellipse Modulation (Se: 65,2%, Es: 88,6%, aROC: 0,831), while the best Confocal Scanning Laser Ophthalmoscopy individual parameters were: Average Disc Diameter (Se: 64%, Sp: 89%, aROC: 0,824), Total Disc Area (Se: 85%, Sp: 66%, aROC: 0,802) and Cup Area (Se: 69%, Sp: 85%, aROC: 0,797). However, the multivariate analysis resulted in better results for the Scanning Laser Polarimetry (Se: 87,5%, Es: 86,4%, aROC: 0,900), Confocal Scanning Laser Ophthalmoscopy (Se: 90%, Sp: 81%, aROC: 0,910), and for the combination of parameters from both devices (Se: 93%, Es: 91%, aROC: 0,970). Conclusion: Structural parameters, as measured by the Confocal Scanning Laser Ophthalmoscopy and Scanning Laser Polarimetry have good ability to differentiate normal individuals from glaucoma patients. However, better results are obtained combining parameters from both devices in a multivariate discriminant formula
Doutorado
Oftalmologia
Doutor em Ciências Médicas
Книги з теми "Glaucome – Diagnostic"
J, Kanski Jack, ed. Glaucoma: A colour manual of diagnosis and treatment. 3rd ed. Oxford: Butterworth-Heinemann, 2004.
Знайти повний текст джерелаF, Lieberman Marc, Drake Michael V, and Becker Bernard 1920-, eds. Becker-Shaffer's diagnosis and therapy of the glaucomas. 8th ed. [Edinburgh]: Mosby/Elsevier, 2009.
Знайти повний текст джерела1912-, Shaffer Robert N., Hoskins H. Dunbar 1939-, and Kass Michael A, eds. Becker-Shaffer's diagnosis and therapy of the glaucomas. 6th ed. St. Louis: Mosby, 1989.
Знайти повний текст джерелаGlobal, AIGS Consensus Meeting (2nd 2005 Ft Lauderdale Fla ). Glaucoma surgery: Open angle glaucoma : reports and consensus statements of the 2nd Global AIGS Consensus Meeting on "glaucoma surgery-open angle glaucoma". The Hague, Netherlands: Kugler Publications, 2005.
Знайти повний текст джерелаGlaucoma. Oxford: Oxford University Press, 2012.
Знайти повний текст джерелаPfeiffer, Norbert. Glaukom: Grundlagen, Diagnostik, Therapie, Compliance. Stuttgart, Germany: Thieme, 2001.
Знайти повний текст джерелаGlaucoma diagnosis and management. Philadelphia: Lippincott Williams & Wilkins, 2005.
Знайти повний текст джерелаRamakrishan, R. Diagnosis and management of glaucoma. Panama City, Panama: Jaypee-Highlights Medical Publishers, Inc., 2013.
Знайти повний текст джерелаElseid, 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.
Повний текст джерелаWeinreb, Robert N., Paul R. Healey, and Fotis Topouzis. Glaucoma screening: Screening for open angle glaucoma, primary angle-closure and primary angle-closure glaucoma : the 5th consensus report of the World Glaucoma Association. Amsterdam, The Netherlands: Kugler Publications, 2008.
Знайти повний текст джерелаЧастини книг з теми "Glaucome – Diagnostic"
Weinreb, Robert N., Christopher Bowd, Sasan Moghimi, Ali Tafreshi, Sebastian Rausch, and Linda M. Zangwill. "Ophthalmic Diagnostic Imaging: Glaucoma." In High Resolution Imaging in Microscopy and Ophthalmology, 107–34. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16638-0_5.
Повний текст джерелаThakur, Sahil. "What’s the Future of Glaucoma Diagnosis and Neuroprotection." In Glaucoma, 115–23. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8457-8_9.
Повний текст джерелаTownsend, Kelly A., Gadi Wollstein, and Joel S. Schuman. "Future Glaucoma Instrumentation: Diagnostic and Therapeutic." In The Glaucoma Book, 995–1009. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-76700-0_90.
Повний текст джерелаMoghimi, Sasan, Mona SafiZadeh, Andrew Camp, and Robert N. Weinreb. "OCT and Glaucoma: Interpretation." In Diagnostics in Ocular Imaging, 579–603. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54863-6_25.
Повний текст джерелаMoghimi, Sasan, Mona SafiZadeh, Andrew Camp, and Robert N. Weinreb. "OCT Artifacts in Glaucoma." In Diagnostics in Ocular Imaging, 631–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54863-6_27.
Повний текст джерелаTello, Celso, Jeffrey M. Liebmann, and Robert Ritch. "New Concepts in the Diagnosis of Angle-Closure Glaucoma: The Role of Ultrasound Biomicroscopy." In Glaucoma, 93–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18633-2_6.
Повний текст джерелаMoghimi, Sasan, Mona SafiZadeh, Andrew Camp, and Robert N. Weinreb. "OCT and Glaucoma: Case Review." In Diagnostics in Ocular Imaging, 605–30. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54863-6_26.
Повний текст джерелаLeonard, Kevin C., and Cindy M. L. Hutnik. "Using Electroretinography for Glaucoma Diagnosis." In The Glaucoma Book, 265–67. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-76700-0_24.
Повний текст джерелаHangai, Masanori. "Glaucoma Diagnosis in Myopic Eyes." In Myopia and Glaucoma, 25–51. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55672-5_3.
Повний текст джерелаIchhpujani, Parul. "Recent Advances in Glaucoma Diagnostics." In Current Advances in Ophthalmic Technology, 69–81. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9795-0_4.
Повний текст джерелаТези доповідей конференцій з теми "Glaucome – Diagnostic"
Girard, Michaël J. A., Lyn Zimmo, Edward T. White, Jean Martial Mari, C. Ross Ethier, and Nicholas G. Strouthidis. "Towards a Biomechanically-Based Diagnosis for Glaucoma: In Vivo Deformation Mapping of the Human Optic Nerve Head." In ASME 2012 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/sbc2012-80557.
Повний текст джерелаThangaraj, Vigneswaran, and V. Natarajan. "Glaucoma diagnosis using support vector machine." In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2017. http://dx.doi.org/10.1109/iccons.2017.8250750.
Повний текст джерелаSingh, Amitojdeep, Sourya Sengupta, and Vasudevan Lakshminarayanan. "Glaucoma diagnosis using transfer learning methods." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2529429.
Повний текст джерелаVlad, Simona, Sorina Demea, Horea Demea, and Rodica Holonec. "Neural network classifier for glaucoma diagnosis." In 2015 E-Health and Bioengineering Conference (EHB). IEEE, 2015. http://dx.doi.org/10.1109/ehb.2015.7391596.
Повний текст джерелаBorwankar, Saumya, Raima Sen, and Bhavin Kakani. "Improved Glaucoma Diagnosis Using Deep Learning." In 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2020. http://dx.doi.org/10.1109/conecct50063.2020.9198524.
Повний текст джерелаLiu, J., F. S. Yin, D. W. K. Wong, Z. Zhang, N. M. Tan, C. Y. Cheung, M. Baskaran, T. Aung, and T. Y. Wong. "Automatic glaucoma diagnosis from fundus image." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6090916.
Повний текст джерелаJun Cheng, Jiang Liu, Damon Wing Kee Wong, Ngan Meng Tan, Beng Hai Lee, Carol Cheung, M. Baskaran, Tien Yin Wong, and Tin Aung. "Focal edge association to glaucoma diagnosis." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091111.
Повний текст джерелаWang, Jingwen, Yuguang Yan, Yanwu Xu, Wei Zhao, Huaqing Min, Mingkui Tan, and Jiang Liu. "Conditional Adversarial Transfer for Glaucoma Diagnosis." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8857308.
Повний текст джерелаKang, Hong, Xiaoxing Li, and Xiu Su. "Cup-disc and retinal nerve fiber layer features fusion for diagnosis glaucoma." In Computer-Aided Diagnosis, edited by Horst K. Hahn and Maciej A. Mazurowski. SPIE, 2020. http://dx.doi.org/10.1117/12.2548546.
Повний текст джерелаStolfi, Carolyn, and Jenn S. Rossmann. "Biomechanics of Glaucoma: A Parametric Study of Flow in Schlemm’s Canal." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12355.
Повний текст джерела