المؤلفون: Ali, Mohammed Yousef Salem

المساهمون: University/Department: Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques

Thesis Advisors: Valls Mateu, Aïda, Abdelnasser Mohamed Mahmoud, Mohamed, Baget Bernaldiz, Marc

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: aprenentatge profund, visió per computador, Anàlisi d'imatges mèdiques, aprendizaje profundo, visión por computador, Análisis de imágenes médicas, deep learning, computer vision, Medical image analysis, Enginyeria i arquitectura

وصف الملف: application/pdf

URL الوصول: http://hdl.handle.net/10803/687502

المؤلفون: Escorcia Gutierrez, José

المساهمون: University/Department: Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques

Thesis Advisors: Puig Valls, Domènec, Romero Aroca, Pedro, Valls Mateu, Aïda

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: Retinopatia diabètica, Anàlisi d'imatges mèdiques, Segmentació de la imatge del fons, Retinopatía diabética, Análisis de imágenes médicas, Segmentación de la imagen del fondo de ojo, Diabetic retinopathy, Medical image analysis, Fundus image segmentation, Ingeniería y arquitectura

Time: 621.3

وصف الملف: application/pdf

URL الوصول: http://hdl.handle.net/10803/671543

المؤلفون: Sánchez Martínez, Sergio

المساهمون: University/Department: Universitat Pompeu Fabra. Departament de Tecnologies de la Informació i les Comunicacions

Thesis Advisors: Duchateau, Nicolas, Piella Fenoy, Gemma, Bijnens, Bart

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: Machine learning, Medical image analysis, Pattern recognition, Multiple kernel learning, Dimensionality reduction, Echocardiography, Early diagnosis, Heart failure, Cardiac resynchronization therapy, Aprendizaje automático, Análisis de imágenes médicas, Reconocimiento de patrones, Aprendizaje de kernel múltiple, Ecocardiografía, Diagnóstico temprano, Insuficiencia cardíaca, Tratamiento de re-sincronización cardíaca

وصف الملف: application/pdf

URL الوصول: http://hdl.handle.net/10803/663748

المؤلفون: Baldeon calisto, Maria, Balseca Zurita, Javier Sebastián, Cruz Patiño, Martin Alejandro

المصدر: ACI Avances en Ciencias e Ingenierías; Vol. 13 No. 2 (2021); 19 ; ACI Avances en Ciencias e Ingenierías; Vol. 13 Núm. 2 (2021); 19 ; 2528-7788 ; 1390-5384 ; 10.18272/aci.v13i2

مصطلحات موضوعية: Convolutional Neural Networks, Deep Learning, Bayesian Optimization, Medical Image Analysis, Data Augmentation, Hyperparameters, redes neuronales convolucionales, aprendizaje profundo de máquina, optimización bayesiana, análisis de imágenes médicas, aumento sintético de datos, hiperparámetros

وصف الملف: application/pdf; text/html; text/xml

Relation: https://revistas.usfq.edu.ec/index.php/avances/article/view/2288/2754; https://revistas.usfq.edu.ec/index.php/avances/article/view/2288/3236; https://revistas.usfq.edu.ec/index.php/avances/article/view/2288/2918; https://revistas.usfq.edu.ec/index.php/avances/article/view/2288

الاتاحة: https://revistas.usfq.edu.ec/index.php/avances/article/view/2288
https://doi.org/10.18272/aci.v13i2.2288

المؤلفون: Ariza López, Luis, Sanz Ramos, Álvaro

المساهمون: Sánchez Ruiz-Granados, Antonio Alejandro

مصطلحات موضوعية: 004(043.3), Deep learning, Convolutional neural networks, Classification, Computer vision, Diabetic retinopathy, Vision Transformers, Transfer learning, Model calibration, Model interpretability, Medical image analysis, Aprendizaje profundo, Redes neuronales convolucionales, Clasificación, Visión artificial, Retinopatía diabética, Aprendizaje transferido, Calibración del modelo, Interpretabilidad, Análisis de imágenes médicas, Informática (Informática), 33 Ciencias Tecnológicas

وصف الملف: application/pdf

Relation: https://hdl.handle.net/20.500.14352/87739

الاتاحة: https://hdl.handle.net/20.500.14352/87739

المؤلفون: Ali, Mohammed Yousef Salem

المساهمون: Valls Mateu, Aïda, Abdelnasser Mohamed Mahmoud, Mohamed, Baget Bernaldiz, Marc, Universitat Rovira i Virgili. Departament d'Enginyeria Informàtica i Matemàtiques

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات موضوعية: aprenentatge profund, visió per computador, Anàlisi d'imatges mèdiques, aprendizaje profundo, visión por computador, Análisis de imágenes médicas, deep learning, computer vision, Medical image analysis, Enginyeria i arquitectura

Time: 004, 62

وصف الملف: 147 p.; application/pdf

Relation: http://hdl.handle.net/10803/687502

الاتاحة: http://hdl.handle.net/10803/687502

المؤلفون: Toledo Cortés, Santiago

المساهمون: González Osorio, Fabio Augusto, Mindlab, orcid:0000-0003-4172-9263, 0001449836, 57207843310, https://www.researchgate.net/profile/Santiago-Toledo-Cortes-2, https://scholar.google.com/citations?user=M7l6jx4AAAAJ&hl=en

مصطلحات موضوعية: 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores, 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas, Histopathology, Ophthalmology, Deep learning, Kernel methods, Medical image analysis, Multimodal learning, Ordinal regression, Probabilistic models, Quantum machine learning, Análisis de imágenes médicas, Histopatologı́a, Métodos de Kernel, Oftalmologı́a, Aprendizaje profundo, Aprendizaje de máquina cuántico, Aprendizaje multimodal, Modelos probabilı́sticos, Regresión ordinal, Teoría de las probabilidades, Inteligencia artificial, Ciencias médicas, Probability theory, Artificial intelligence, Medical sciences

وصف الملف: xvi, 123 páginas; application/pdf

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الاتاحة: https://repositorio.unal.edu.co/handle/unal/85336
https://repositorio.unal.edu.co/

المؤلفون: Coll Corbilla, Jordi

المساهمون: Nuñez Do Rio, Joan Manuel, Ventura Royo, Carles

المصدر: O2, repositorio institucional de la UOC
Universitat Oberta de Catalunya (UOC)

مصطلحات موضوعية: aprendizaje profundo, aprenentatge profund, deep learning, eye diseases, Inteligencia artificial -- TFM, anàlisi d'imatges mèdiques, xarxes neuronals convolucionals, malalties oculars, redes neuronales convolucionales, medical imaging analysis, classificació d'imatges, retinography, análisis de imágenes médicas, Artificial intelligence -- TFM, clasificación de imágenes, Intel·ligència artificial -- TFM, convolutional neural networks, retinografia, retinografía, enfermedades oculares, image classification

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=RECOLECTA___::c484b563336c43320e819c8078292393
http://hdl.handle.net/10609/113126

المؤلفون: Cano Ramírez, Fabián Alberto

المساهمون: Cruz Roa, Angel Alfonso

مصطلحات موضوعية: Histopatología, Patología digital, Aprendizaje de una sola vez, Aprendizaje semi-supervisado, Análisis de imágenes médicas, Clasificación automática, Histopathology, Digital pathology, One shot learning, Semi-supervised learning, Medical image analysis, Automatic classification

وصف الملف: 74 páginas; application/pdf

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In 2011 6th Colombian Computing Congress (CCC) (pp. 1–7). IEEE. https://doi.org/10.1109/COLOMCC.2011.5936285; Cruz Roa, A., Romero, E., & González, F. (2013). An adaptive image representation learned from data for cervix cancer tumor detection, 8676, 86760Q. https://doi.org/10.1117/12.2007081; Daniel, C., Macary, F., García, M., Klossa, J., Laurinavičius, A. (2011). Recent advances in standards for collaborative Digital Anatomic Pathology. Diagnostic Pathology. https://doi.org/10.1186/1746-1596-6-S1-S17; Demir, C., & Yener, B. (2005). Automated cancer diagnosis based on histopathological images: a systematic survey. Dept. Comput. Sci., Rensselaer Polytechnic Inst., Troy, NY, USA, Tech. Rep., TR-05-09 (February), 1–16. https://doi.org/10.1.1.61.1199; Doyle, S., Monaco, J., Feldman, M., Tomaszewski, J., & Madabhushi, A. (2011). An active learning based classification strategy for the minority class problem: Application to histopathology annotation. BMC Bioinformatics, 12. https://doi.org/10.1186/1471-2105-12-424; Fuchs, T. J., & Buhmann, J. M. (2011, October). Computational pathology: Challenges and promises for tissue analysis. Computerized Medical Imaging and Graphics. https://doi.org/10.1016/j.compmedimag.2011.02.006; García, M., Bueno, G., Peces, C., González, J., Carbajo, M. (2005). Preparaciones digitales en los servicios de Anatomía Patológica (II). Análisis de soluciones existentes. Revista Española de Patología, Vol. 38, n.o 4. Retrieved from http://www.patologia.es/volumen38/vol38-num4/38-4n03.htm; García, G., & Sánchez, F. (2012). Enteritis necrótica en aves de ornato. Universidad Nacional Autónoma de México. Retrieved from https://www.engormix.com/avicultura/articulos/enteritis-necrotica-aves-ornato-t29602.htm; Jia Deng, Wei Dong, Socher, R., Li-Jia Li, Kai Li, & Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). 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College of American Pathologists. https://doi.org/10.5858/arpa.2014-0606-RA; Nimo, Ana María. (2016). La patología digital revoluciona el proceso de diagnóstico. Retrieved from http://www.innovacionensalud.elmundo.es/salud-digital/la-patologia-digital-revoluciona-el-proceso-de-diagnostico; Padmanabhan, R. 1984-. (2014). Active and Transfer learning Methods for Computational Histology. Retrieved from https://uh-ir.tdl.org/uh-ir/handle/10657/787; Pantanowitz, L., Evans, A., Pfeifer, J., Collins, L., Valenstein, P., Kaplan, K., … Colgan, T. (2011). Review of the current state of whole slide imaging in pathology. Journal of Pathology Informatics, 2(1), 36. https://doi.org/10.4103/2153-3539.83746; Journal of Pathology Informatics, 2(1), 36. https://doi.org/10.4103/2153-3539.83746 21. Pardo, C., de Vries, E., Duarte, J. M., & Piñeros, M. (2015). Cáncer en la Unidad de Cáncer del Hospital Departamental de Villavicencio, Colombia, 2006-2008. 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D., Gavrielides, M. A., & Hewitt, S. M. (2015). Evaluating whole slide imaging: A working group opportunity. Journal of Pathology Informatics, 6, 4. Retrieved from http://www.jpathinformatics.org/text.asp?2015/6/1/4/151880; Vinyals, O., Blundell, C., Lillicrap, T., Kavukcuoglu, K., & Wierstra, D. (2016). Matching Networks for One Shot Learning. https://doi.org/10.1109/CVPR.2016.95; Wright, A., Magee, D., Quirke, P., & Treanor, D. E. (2014). Towards automatic patient selection for chemotherapy in colorectal cancer trials. In M. N. Gurcan & A. Madabhushi (Eds.) (p. 90410A). https://doi.org/10.1117/12.2043220; Pardo, Constanza, de Vries, Esther, Duarte, José María, & Piñeros, Marion. (2015). Cáncer en la Unidad de Cáncer del Hospital Departamental de Villavicencio, Colombia, 2006-2008. Revista Colombiana de Cancerología, 19(3), 125-132.; Piñeros, M., Sánchez, R., Perry, F., García, O., Ocampo, R., & Cendales, R. (2011). Demoras en el diagnóstico y tratamiento de mujeres con cáncer de mama en Bogotá, Colombia. Salud Pública de México, 53(6), 478-485.; Bromley, J., Bentz, J., Bottou, L., Guyon, I., LeCun, Y., Moore, C., Sackinger, E., & Shah, R. (1993). Signature verification using a siamese time delay neural network. International Journal of Pattern Recognition and Artificial Intelligence, 7 (04):669–688.; Koch, G., Zemel, R., & Salakhutdinov, R. (2015). Siamese Neural Networks for One-shot Image Recognition. Department of Computer Science, University of Toronto. Toronto, Ontario, Canada. Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37; Computer Vision. (2018). Siamese Neural Networks. Lugar de publicación: Computer Vision, Tecnalia. https://computervision.tecnalia.com/es/2018/08/siamese-neural-networks/; Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Gonzalez, F., & Madabhushi, A. (2017). Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent. In: Scientific Reports 7, 46450. https://doi.org/https://doi.org/doi:10.1038/srep46450; Cruz-Roa, A., Arevalo, J., Judkinsc, A., Madabhushid, A., and Gonzalez, F. (2015). A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning, 11th International Symposium on Medical Information Processing and Analysis 9681; Krizhevsky, A., Sutskever, I. Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems 25, 1106-1114; Ciresan, D., Giusti, A., Gambardella, L. Schmidhuber, J. (2013). Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, 8150, 411-418; Wang, H. et al. (2014). Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. In: Journal of Medical Imaging 1, 34003; Cruz-Roa, A. et al. (2014). Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: Proc. SPIE 9041, 904103-904115; DeSantis, C., Ma, J., Bryan, L. Jemal. (2014). A. Breast Cancer Statistics, 2013. In: A Cancer Journal for Clinicians 64(1), 52-62.; DeSantis, C., Siegel, R., Bandi, P. Jemal. (2011). A. Breast cancer statistics, 2011. In: A Cancer Journal for Clinicians 61, 408-418; Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2013). Decaf: A deep convolutional activation feature for generic visual recognition. CoRR abs/1310.1531; Byra, M., Styczynski, G., Szmigielski, C., Kalinowski, P., Michałowski, Ł., Paluszkiewicz, R., Nowicki, A. (2018). Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. International journal of computer assisted radiology and surgery, 13(12), 1895–1903. doi:10.1007/s11548-018-1843-2; Huynh BQ, Li H, Giger ML. (2016). Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging. doi:10.1117/1.JMI.3.3.034501.; Deshpande, A. (n.d.). A Beginner's Guide To Understanding Convolutional Neural Networks. Recuperado de https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/; Hong, S. (2017). A Vision for Making Deep Learning Simple From Machine Learning Practitioners to Business Analysts. Databricks. Recuperado de https://databricks.com/blog/2017/06/06/databricks-vision-simplify-large-scale-deep-learning.html; Cano, F., Madabhushi, A., & Cruz-Roa, A. (2018). A comparative analysis of sensitivity of convolutional neural networks for histopathology image classification in breast cancer. Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750W; doi:10.1117/12.2511647; https://doi.org/10.1117/12.2511647; Neurohive. (2018). VGG16 – Convolutional Network for Classification and Detection. Recuperado de https://neurohive.io/en/popular-networks/vgg16/; Recuperado de https://neurohive.io/en/popular-networks/vgg16/ 49. Lamba, H. (2019). One Shot Learning with Siamese Networks using Keras. Publicado en: Towards Data Science. Recuperado de https://towardsdatascience.com/one-shot-learning-with-siamese-networks-using-keras-17f34e75bb3d; Freitas, T. (2018). Implementation of Siamese Neural Networks for One-shot Image Recognition. Recuperado de https://github.com/Goldesel23/Siamese-Networks-for-One-Shot-Learning; Rao, S. (2018). MITOS-RCNN: A Novel Approach to Mitotic Figure Detection in Breast Cancer Histopathology Images using Region Based Convolutional Neural Networks. Elsevier Medical Image Analysis Journal. https://arxiv.org/abs/1807.01788; https://repositorio.unillanos.edu.co/handle/001/4561; Universidad de los Llanos; Repositorio digital Universidad de los Llanos; https://repositorio.unillanos.edu.co

الاتاحة: https://repositorio.unillanos.edu.co/handle/001/4561
https://repositorio.unillanos.edu.co

المؤلفون: Otálora Montenegro, Juan Sebastian

المساهمون: González Osorio, Fabio Augusto

المصدر: Repositorio UN
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombia

مصطلحات موضوعية: Aprendizaje en Linea, Longitud esperada del gradiente, Expected Gradient Length, Active Learning, Aprendizaje Activo, Machine Learning, Deep Learning, Medical Imaging, 51 Matemáticas / Mathematics, 62 Ingeniería y operaciones afines / Engineering, Redes Neuronales Profundas, Aprendizaje de la Representación, Aprendizaje de máquina, 6 Tecnología (ciencias aplicadas) / Technology, On-line Learning, Análisis de Imágenes Médicas, Eye Fundus imaging

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6c582f2123d0b293ce9c1f045b8c07de
http://bdigital.unal.edu.co/57883/

المؤلفون: Cárdenas Peña, David Augusto

المساهمون: Castellanos Domínguez, César Germán (Thesis advisor)

مصطلحات موضوعية: 6 Tecnología (ciencias aplicadas) / Technology, 62 Ingeniería y operaciones afines / Engineering, Análisis de imágenes medicas, Teoría de kernels, Agrupamiento de RNM, Segmentación basada en atlases, Selección de plantillas, Segmentación Bayesiana, Segmentación basada en parches, Diagnóstico asistido por computador, Función de costo empleando medidas de información, Redes neuronales, Medical image analysis, Kernel theory, MRI clustering, Atlas-based segmentation, Template selection, Bayesian segmentation, Patch-based segmentation, Computer-aided diagnosis, Information-based cost function, Neural networks

وصف الملف: application/pdf

Relation: Universidad Nacional de Colombia Sede Manizales Facultad de Ingeniería y Arquitectura Departamento de Ingeniería Eléctrica, Electrónica y Computación Ingeniería Electrónica; Ingeniería Electrónica; Cárdenas Peña, David Augusto (2016) Kernel-based image analysis towards MRI segmentation and classification. Doctorado thesis, Universidad Nacional de Colombia - Sede Manizales.; https://repositorio.unal.edu.co/handle/unal/56541; http://bdigital.unal.edu.co/52348/

الاتاحة: https://repositorio.unal.edu.co/handle/unal/56541
http://bdigital.unal.edu.co/52348/

المؤلفون: Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili., Ali, Mohammed Yousef Salem

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات الفهرس: 62, 004, Enginyeria i arquitectura, Medical image analysis, computer vision, deep learning, Análisis de imágenes médicas, visión por computador, aprendizaje profundo, Anàlisi d'imatges mèdiques, visió per computador, aprenentatge profund, info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis

URL: http://hdl.handle.net/20.500.11797/TDX4137
http://hdl.handle.net/10803/687502

المؤلفون: Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili., Escorcia Gutierrez, José

المصدر: TDX (Tesis Doctorals en Xarxa)

مصطلحات الفهرس: 621.3, 62, 61, Ingeniería y arquitectura, Fundus image segmentation, Medical image analysis, Diabetic retinopathy, Segmentación de la imagen del fondo de ojo, Análisis de imágenes médicas, Retinopatía diabética, Segmentació de la imatge del fons, Anàlisi d'imatges mèdiques, Retinopatia diabètica, info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis

URL: http://hdl.handle.net/20.500.11797/TDX3244
http://hdl.handle.net/10803/671543