Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica
Pasantía institucional (Ingeniero Biomédico)-- Universidad Autónoma de Occidente, 2022
- Autores:
-
Salinas Lopez, Vanessa
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- spa
- OAI Identifier:
- oai:red.uao.edu.co:10614/14129
- Acceso en línea:
- https://hdl.handle.net/10614/14129
https://red.uao.edu.co/
- Palabra clave:
- Ingeniería Biomédica
Segmentación de imágenes
Procesamiento de imágenes - Técnicas digitales
Mamografía
Image processing - Digital techniques
Breast - Radiography
Radiómica
Densidad mamaria
Inteligencia artificial
Máquinas de aprendizaje
Radiología
Imágenes diagnósticas
- Rights
- openAccess
- License
- Derechos reservados - Universidad Autónoma de Occidente, 2022
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Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica |
| title |
Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica |
| spellingShingle |
Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica Ingeniería Biomédica Segmentación de imágenes Procesamiento de imágenes - Técnicas digitales Mamografía Image processing - Digital techniques Breast - Radiography Radiómica Densidad mamaria Inteligencia artificial Máquinas de aprendizaje Radiología Imágenes diagnósticas |
| title_short |
Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica |
| title_full |
Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica |
| title_fullStr |
Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica |
| title_full_unstemmed |
Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica |
| title_sort |
Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómica |
| dc.creator.fl_str_mv |
Salinas Lopez, Vanessa |
| dc.contributor.advisor.none.fl_str_mv |
Pulgarín Giraldo, Juan Diego |
| dc.contributor.author.none.fl_str_mv |
Salinas Lopez, Vanessa |
| dc.subject.spa.fl_str_mv |
Ingeniería Biomédica Segmentación de imágenes |
| topic |
Ingeniería Biomédica Segmentación de imágenes Procesamiento de imágenes - Técnicas digitales Mamografía Image processing - Digital techniques Breast - Radiography Radiómica Densidad mamaria Inteligencia artificial Máquinas de aprendizaje Radiología Imágenes diagnósticas |
| dc.subject.armarc.spa.fl_str_mv |
Procesamiento de imágenes - Técnicas digitales Mamografía |
| dc.subject.armarc.eng.fl_str_mv |
Image processing - Digital techniques Breast - Radiography |
| dc.subject.proposal.spa.fl_str_mv |
Radiómica Densidad mamaria Inteligencia artificial Máquinas de aprendizaje Radiología Imágenes diagnósticas |
| description |
Pasantía institucional (Ingeniero Biomédico)-- Universidad Autónoma de Occidente, 2022 |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-08-09T18:50:51Z |
| dc.date.available.none.fl_str_mv |
2022-08-09T18:50:51Z |
| dc.date.issued.none.fl_str_mv |
2022-06-03 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Pregrado |
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https://hdl.handle.net/10614/14129 |
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Universidad Autónoma de Occidente |
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Repositorio Educativo Digital |
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https://red.uao.edu.co/ |
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https://hdl.handle.net/10614/14129 https://red.uao.edu.co/ |
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Universidad Autónoma de Occidente Repositorio Educativo Digital |
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spa |
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spa |
| dc.relation.cites.spa.fl_str_mv |
Salinas López, V. (2022). Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema bi-rads mediante radiómica. (Pasantía institucional). Universidad Autónoma de Occidente. Cali. Colombia. https://red.uao.edu.co/handle/10614/14129 |
| dc.relation.references.none.fl_str_mv |
[1] Ministerio de Salud y Protección Social, “Cáncer de mama,” MinSalud. https://www.minsalud.gov.co/salud/publica/ssr/Paginas/Cancer-demama. aspx [2] C. J. D’Orsi, E. A. Sickels, y L. W. Bassett, “ACR BI-RADS® Mammography,” in ACR BI-RADS® Atlas: Breast Imaging Reporting and Data System, 5th ed., Reston, VA: American College of Radiology, 2013. [3] American Cancer Society, “Breast Cancer Facts and Figures 2019-2020,” American Cancer Society, Inc., Atlanta, US, 2019. [En línea]. Disponible en: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-andstatistics/ breast-cancer-facts-and-figures/breast-cancer-facts-and-figures- 2019-2020.pdf [4] M. Posso et al., “Mammographic breast density: How it affects performance indicators in screening programmes?,” European Journal of Radiology, vol. 110, pp. 81–87, 2019, doi: 10.1016/j.ejrad.2018.11.012. [5] C. Lei et al., “Mammography-based radiomic analysis for predicting benign BIRADS category 4 calcifications,” European Journal of Radiology, vol. 121, no. 95, p. 108711, 2019, doi: 10.1016/j.ejrad.2019.108711. [6] N. Vállez, G. Bueno, O. Déniz-Suárez, J. A. Seone, J. Dorado, y A. Pazos, “A tree classifier for automatic breast tissue classification based on BIRADS categories,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6669 LNCS, pp. 580–587, 2011, doi: 10.1007/978-3-642-21257-4_72. [7] Y. C. Zeng, “Mammogram Density Classification using Double Support Vector Machines,” 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018, no. Gcce 2018, pp. 77–78, 2018, doi: 10.1109/GCCE.2018.8574642. [8] M. Alhelou, M. Deriche, y L. Ghouti, “Breast density classification using a bag of features and an SVM classifier,” 2017 9th IEEE-GCC Conference and Exhibition, GCCCE 2017, pp. 0–4, 2018, doi: 10.1109/IEEEGCC.2017.8447974. [9] G. A. Thibodeau y K. T. Patton, “Anatomia y Fisiologia.” ELSEVIER, España, 2007. [10] L. L. Fajardo y L. Yang, “Breast Imaging,” Radiology Key, 2016. https://radiologykey.com/11-breast-imaging/ (Accedido Nov. 06, 2020). [11] A. Ruibal, J. D. Faes, y A. Tejerina, Eds., “Cáncer de mama: Aspectos de interés actual,” Fundación de Estudios Mastológicos. Fundación de Estudios Mastológicos, 2012. [12] S. Robertson y F. Gaillard, “Mammography | Radiology Reference Article,” Radiopaedia. https://radiopaedia.org/articles/mammography?lang=us (Accedido Nov. 11, 2020). [13] H. Aichinger y J. Dierker., “Radiation Exposure and Image Quality in X-Ray Diagnostic Radiology.” Springer, 2008. [14] F. Sciacca y M. M. Nadrljanski, “Anode | Radiology Reference Article,” Radiopaedia. https://radiopaedia.org/articles/anode-1 (Accedido Nov. 11, 2020). [15] P. Sprawls, “Physical Principles of Medical Imaging: Mammography Physics and Technology,” Sprawls Educational Foundation. Sprawls Educational Foundation, Atlanta, GA, USA. [16] S. J. Vinnicombe, “Breast density: why all the fuss?,” Clinical Radiology, vol. 73, no. 4. W.B. Saunders Ltd, pp. 334–357, 2018. doi: 10.1016/j.crad.2017.11.018. [17] V. Paulina Neira, “Densidad mamaria y riesgo de cáncer mamario,” Revista Médica Clínica Las Condes, vol. 24, no. 1, 2013, doi: 10.1016/s0716- 8640(13)70137-8. [18] N. F. Boyd, L. J. Martin, M. Bronskill, M. J. Yaffe, N. Duric, y S. Minkin, “Breast Tissue Composition and Susceptibility to Breast Cancer,” JNCI Journal of the National Cancer Institute, vol. 102, no. 16, p. 1224, Aug. 2010, doi: 10.1093/JNCI/DJQ239. [19] World Health Organization, “Global Profile Cancer 2020 WHO,” Switzerland, 2020. [20] World Health Organization, “Cancer Country Profile 2020 Colombia,” Switzerland, 2020. [21] PDQ® Adult Treatment Editorial Board, “PDQ Breast Cancer Treatment (Adult),” National Cancer Institute, 2020. https://www.cancer.gov/types/breast/patient/breast-treatment-pdq#Keypoint2 (Accedido Nov. 06, 2020). [22] D. A. Spak, J. S. Plaxco, L. Santiago, M. J. Dryden, y B. E. Dogan, “BI-RADS® fifth edition: A summary of changes,” Diagnostic and Interventional Imaging, vol. 98, no. 3, pp. 179–190, 2017, doi: 10.1016/j.diii.2017.01.001. [23] Geoff Dougherty, “Digital Image Processing for Medical Applications,” Cambridge University Press. Cambridge University Press, Cambridge, 2009. [24] J. Rogowska, “Overview and Fundamentals of Medical Image Segmentation,” in Handbook of Medical Image Processing and Analysis, 2nd ed., 2009, pp. 73–90. doi: 10.1016/B978-0-12-373904-9.50013-1. [25] D. A. Ragab, M. Sharkas, y O. Attallah, “Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers,” Diagnostics 2019, Vol. 9, Page 165, vol. 9, no. 4, p. 165, Oct. 2019, doi: 10.3390/DIAGNOSTICS9040165. [26] P. Lambin et al., “Radiomics: Extracting more information from medical images using advanced feature analysis,” European Journal of Cancer, vol. 48, no. 4, pp. 441–446, Mar. 2012, doi: 10.1016/j.ejca.2011.11.036. [27] D. Dong et al., “Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer,” China. Oncol, vol. 10, p. 412, 2020, doi: 10.3389/fonc.2020.00412. [28] P. Lambin et al., “Radiomics: Extracting more information from medical images using advanced feature analysis”, doi: 10.1016/j.ejca.2011.11.036. [29] A. Zwanenburg, S. Leger, M. Vallières, y S. Löck, “Image biomarker standardisation initiative,” Dec. 2016, doi: 10.1148/radiol.2020191145. [30] V. Kumar et al., “Radiomics: The process and the challenges,” Magnetic Resonance Imaging, vol. 30, no. 9, pp. 1234–1248, Nov. 2012, doi: 10.1016/J.MRI.2012.06.010. [31] D. Elvira Contreras de Miguel Tutor y F. Ruiz Santiago, “Avances y retos en el campo de la Radiómica y Radiogenómica,” 2018. [32] N. Gutierrez y C. Gonzalo, “Extracción de Características Texturales de Imágenes de Resonancia Magnética del Cerebro,” Madrid, 2019. [33] J. J. M. van Griethuysen et al., “Computational radiomics system to decode the radiographic phenotype,” Cancer Research, vol. 77, no. 21, pp. e104– e107, Nov. 2017, doi: 10.1158/0008-5472.CAN-17-0339. [34] S. Shalev-Shwartz y S. Ben-David, Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014. Accedido: Apr. 28, 2022. [En línea]. Disponible en: http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning [35] R. Ortiz-Ramón et al., “Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images,” Computerized Medical Imaging and Graphics, vol. 74, pp. 12–24, Jun. 2019, doi: 10.1016/j.compmedimag.2019.02.006. [36] A. C. Müller, Introduction to machine learning with Python: a guide for data scientists. Sebastopol, California: O’Reilly Media, 2017. [37] UniMOOC, “Lección 5: Aprendizaje supervisado: Algoritmos de clasificación y regresión,” UniMOOC. [38] C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer New York, 2006. [39] A.Géron, “Hands-on machine learning with Scikit-Learn, Keras and TensorFlow: concepts, tools, and techniques to build intelligent systems,” O’Reilly Media, p. 851, 2019. [40] B. O. Olivares, A. Vega, M. Angélica, R. Calderón, J. C. Rey, y D. Lobo, “Classification of areas affected by banana wilt: an application with Machine Learning algorithms in Venezuela,” Revista especializada de ingeniería y ciencias de la tierra, vol. 1, no. 1, pp. 1–17, Jan. 2021, [En línea]. Disponible en: https://revistas.up.ac.pa/index.php/REICTORCID:https://orcid.org/0000- 0001-7271-3606 [41] G. Menardi, N. Torelli, G. Menardi, y N. Torelli, “Training and assessing classification rules with imbalanced data,” Data Min Knowl Disc, vol. 28, pp. 92–122, 2014, doi: 10.1007/s10618-012-0295-5. [42] J. Zhang y L. Chen, “Clustering-based undersampling with random over sampling examples and support vector machine for imbalanced classification of breast cancer diagnosis,” Computer Assisted Surgery, vol. 24, no. sup2, pp. 62–72, Oct. 2019, doi: 10.1080/24699322.2019.1649074. [43] V. Fonti y E. Belitser, “Feature Selection using LASSO,” VU Amsterdam, Amsterdam, 2017. [44] N. M. Ali, N. A. A. Aziz, y R. Besar, “Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 20, no. 2, pp. 712–719, Nov. 2020, doi: 10.11591/IJEECS.V20.I2.PP712-719. [45] R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society. Series B (Methodological), vol. 58, no. 1, pp. 267–288, 1996, [En línea]. Disponible en: http://www.jstor.org/stable/2346178 [46] M. Grandini, E. Bagli, y G. Visani, “Metrics for Multi-Class Classification: an Overview,” Aug. 2020, doi: 10.48550/arxiv.2008.05756. [47] S. Bravo-Grau y J. P. Cruz, “Estudios de exactitud diagnóstica: Herramientas para su Interpretación,” Revista Chilena de Radiología, vol. 21, no. 4, pp. 158– 164, 2015. [48] W. Zhu, N. Zeng, y N. Wang, “Sensitivity, Specificity, Accuracy, Associated Confidence Interval and ROC Analysis with Practical SAS ® Implementations,” 2010. [49] A. Kulkarni, D. Chong, y F. A. Batarseh, “Foundations of data imbalance and solutions for a data democracy,” Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering, pp. 83–106, Jan. 2020, doi: 10.1016/B978-0-12-818366-3.00005-8. [50] P. Ranganathan, C. Pramesh, y R. Aggarwal, “Common pitfalls in statistical analysis: Measures of agreement,” Perspect Clin Res, vol. 8, no. 4, pp. 187– 191, Oct. 2017, doi: 10.4103/PICR.PICR_123_17. [51] T. Fawcett, “ROC Graphs: Notes and Practical Considerations for Researchers,” Machine Learning, vol. 31, pp. 1–38, Mar. 2004. [52] H. J. Jeong, T. Y. Kim, H. G. Hwang, H. J. Choi, H. S. Park, y H. K. Choi, “Comparison of thresholding methods for breast tumor cell segmentation,” Proceedings of the 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM 2005, pp. 392–395, 2005, doi: 10.1109/HEALTH.2005.1500489. [53] M. Parisa Beham, R. Tamilselvi, S. M. Mansoor Roomi, y A. Nagaraj, “Accurate Classification of Cancer in Mammogram Images,” Lecture Notes in Networks and Systems, vol. 65, pp. 71–77, 2019, doi: 10.1007/978-981-13- 3765-9_8/FIGURES/4. [54] P. Ghosh, A. Karim, T. Syeda, S. Afrin, y M. Saifuzzaman, “Expert cancer model using supervised algorithms with a LASSO selection approach,” International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 3, pp. 2632–2640, 2021, doi: 10.11591/ijece.v11i3.pp2631-2639. [55] N. Santamaria-Macias, J. F. Orejuela-Zapata, J. D. Pulgarin-Giraldo, y A. M. Granados-Sanchez, “Critical Diagnosis in Brain MRI Studies based on Image Signal Intensity and Supervised Learning,” 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings, Aug. 2020, doi: 10.1109/COLCACI50549.2020.9247930. [56] C. K. Valencia-Marin, J. D. Pulgarin-Giraldo, L. F. Velasquez-Martinez, A. M. Alvarez-Meza, y G. Castellanos-Dominguez, “An Enhanced Joint Hilbert Embedding-Based Metric to Support Mocap Data Classification with Preserved Interpretability,” Sensors 2021, Vol. 21, Page 4443, vol. 21, no. 13, p. 4443, Jun. 2021, doi: 10.3390/S21134443. [57] R. Chandra Joshi, R. Mishra, P. Gandhi, V. K. Pathak, R. Burget, y M. K. Dutta, “Ensemble based machine learning approach for prediction of glioma and multi-grade classification,” Computers in Biology and Medicine, vol. 137, p. 104829, Oct. 2021, doi: 10.1016/J.COMPBIOMED.2021.104829. [58] M. Rohini y D. Surendran, “Classification of Neurodegenerative Disease Stages using Ensemble Machine Learning Classifiers,” Procedia Computer Science, vol. 165, pp. 66–73, 2019, doi: 10.1016/J.PROCS.2020.01.071. [59] H. D. Nelson, E. S. O’meara, K. Kerlikowske, S. Balch, y D. Miglioretti, “Factors Associated with Rates of False-positive and False-negative Results from Digital Mammography Screening: An Analysis of Registry Data”, doi: 10.7326/M15-0971. [60] A. D. Gordon, L. Breiman, J. H. Friedman, R. A. Olshen, y C. J. Stone, “Classification and Regression Trees.,” Biometrics, vol. 40, no. 3, p. 874, Sep. 1984, doi: 10.2307/2530946. |
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Pulgarín Giraldo, Juan Diegovirtual::4177-1Salinas Lopez, Vanessaa936157080854e705a7278eb755dcf632022-08-09T18:50:51Z2022-08-09T18:50:51Z2022-06-03https://hdl.handle.net/10614/14129Universidad Autónoma de OccidenteRepositorio Educativo Digitalhttps://red.uao.edu.co/74 páginasapplication/pdfspaUniversidad Autónoma de OccidenteIngeniería BiomédicaDepartamento de Automática y ElectrónicaFacultad de IngenieríaCaliDerechos reservados - Universidad Autónoma de Occidente, 2022https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Ingeniería BiomédicaSegmentación de imágenesProcesamiento de imágenes - Técnicas digitalesMamografíaImage processing - Digital techniquesBreast - RadiographyRadiómicaDensidad mamariaInteligencia artificialMáquinas de aprendizajeRadiologíaImágenes diagnósticasSistema de clasificación de mamografías según la densidad del tejido definida en el sistema BI-RADS mediante radiómicaTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttps://purl.org/redcol/resource_type/TPhttp://purl.org/coar/version/c_71e4c1898caa6e32Pasantía institucional (Ingeniero Biomédico)-- Universidad Autónoma de Occidente, 2022PregradoIngeniero(a) Biomédico(a)Salinas López, V. (2022). Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema bi-rads mediante radiómica. (Pasantía institucional). Universidad Autónoma de Occidente. Cali. Colombia. https://red.uao.edu.co/handle/10614/14129[1] Ministerio de Salud y Protección Social, “Cáncer de mama,” MinSalud. https://www.minsalud.gov.co/salud/publica/ssr/Paginas/Cancer-demama. aspx[2] C. J. D’Orsi, E. A. Sickels, y L. W. Bassett, “ACR BI-RADS® Mammography,” in ACR BI-RADS® Atlas: Breast Imaging Reporting and Data System, 5th ed., Reston, VA: American College of Radiology, 2013.[3] American Cancer Society, “Breast Cancer Facts and Figures 2019-2020,” American Cancer Society, Inc., Atlanta, US, 2019. [En línea]. Disponible en: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-andstatistics/ breast-cancer-facts-and-figures/breast-cancer-facts-and-figures- 2019-2020.pdf[4] M. 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Stone, “Classification and Regression Trees.,” Biometrics, vol. 40, no. 3, p. 874, Sep. 1984, doi: 10.2307/2530946.Comunidad generalPublicationhttps://scholar.google.com.co/citations?user=Bwuc2BkAAAAJ&hl=envirtual::4177-10000-0002-6409-5104virtual::4177-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000207497virtual::4177-133e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4177-133e9b6b4-bd6d-4b86-b500-ae237e1e9a98virtual::4177-1LICENSElicense.txtlicense.txttext/plain; charset=utf-81665https://red.uao.edu.co/bitstreams/8d2e18b4-3161-4ecb-91d8-b9b0cb3fd1a3/download20b5ba22b1117f71589c7318baa2c560MD52ORIGINALT10332_Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema bi-rads mediante radiómica.pdfT10332_Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema bi-rads mediante radiómica.pdfTexto archivo completo del trabajo de grado, PDFapplication/pdf1457661https://red.uao.edu.co/bitstreams/1d42d9ff-5713-46b3-9690-6686489c6bb9/download1585f38d0784885759a090f03067dd26MD53TA10332_Autorización trabajo de grado.pdfTA10332_Autorización trabajo de grado.pdfAutorización publicación del trabajo de gradoapplication/pdf262101https://red.uao.edu.co/bitstreams/b6dbde23-234b-4450-8597-80035bae708a/downloadf3d3d9596a84b843f502622e43345d99MD54TEXTT10332_Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema bi-rads mediante radiómica.pdf.txtT10332_Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema bi-rads mediante radiómica.pdf.txtExtracted texttext/plain101472https://red.uao.edu.co/bitstreams/55c62b2b-0c85-49cb-b1f6-f408fa7b4012/downloadac975152581db4d6dc530400ae5cecacMD55TA10332_Autorización trabajo de grado.pdf.txtTA10332_Autorización trabajo de grado.pdf.txtExtracted texttext/plain4080https://red.uao.edu.co/bitstreams/dd1c76b4-5419-4bc5-8b7e-3cd74283b5c6/download55c7f632528328801392f2ed91ed337bMD57THUMBNAILT10332_Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema bi-rads mediante radiómica.pdf.jpgT10332_Sistema de clasificación de mamografías según la densidad del tejido definida en el sistema bi-rads mediante radiómica.pdf.jpgGenerated Thumbnailimage/jpeg5933https://red.uao.edu.co/bitstreams/0e67e169-fd43-4246-8c12-05dcda34a101/download7f58c188e41bfe5b6d0e59800f67d2c8MD56TA10332_Autorización trabajo de grado.pdf.jpgTA10332_Autorización trabajo de grado.pdf.jpgGenerated Thumbnailimage/jpeg13299https://red.uao.edu.co/bitstreams/4a5694dc-8ce0-449e-9091-847698cd3d2c/download36f12bc473456b60275bea8c0d3e5dcaMD5810614/14129oai:red.uao.edu.co:10614/141292024-03-13 14:16:35.127https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - Universidad Autónoma de Occidente, 2022open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.coRUwgQVVUT1IgYXV0b3JpemEgYSBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgT2NjaWRlbnRlLCBkZSBmb3JtYSBpbmRlZmluaWRhLCBwYXJhIHF1ZSBlbiBsb3MgdMOpcm1pbm9zIGVzdGFibGVjaWRvcyBlbiBsYSBMZXkgMjMgZGUgMTk4MiwgbGEgTGV5IDQ0IGRlIDE5OTMsIGxhIERlY2lzacOzbiBhbmRpbmEgMzUxIGRlIDE5OTMsIGVsIERlY3JldG8gNDYwIGRlIDE5OTUgeSBkZW3DoXMgbGV5ZXMgeSBqdXJpc3BydWRlbmNpYSB2aWdlbnRlIGFsIHJlc3BlY3RvLCBoYWdhIHB1YmxpY2FjacOzbiBkZSBlc3RlIGNvbiBmaW5lcyBlZHVjYXRpdm9zLiBQQVJBR1JBRk86IEVzdGEgYXV0b3JpemFjacOzbiBhZGVtw6FzIGRlIHNlciB2w6FsaWRhIHBhcmEgbGFzIGZhY3VsdGFkZXMgeSBkZXJlY2hvcyBkZSB1c28gc29icmUgbGEgb2JyYSBlbiBmb3JtYXRvIG8gc29wb3J0ZSBtYXRlcmlhbCwgdGFtYmnDqW4gcGFyYSBmb3JtYXRvIGRpZ2l0YWwsIGVsZWN0csOzbmljbywgdmlydHVhbCwgcGFyYSB1c29zIGVuIHJlZCwgSW50ZXJuZXQsIGV4dHJhbmV0LCBpbnRyYW5ldCwgYmlibGlvdGVjYSBkaWdpdGFsIHkgZGVtw6FzIHBhcmEgY3VhbHF1aWVyIGZvcm1hdG8gY29ub2NpZG8gbyBwb3IgY29ub2Nlci4gRUwgQVVUT1IsIGV4cHJlc2EgcXVlIGVsIGRvY3VtZW50byAodHJhYmFqbyBkZSBncmFkbywgcGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBvYmpldG8gZGUgbGEgcHJlc2VudGUgYXV0b3JpemFjacOzbiBlcyBvcmlnaW5hbCB5IGxhIGVsYWJvcsOzIHNpbiBxdWVicmFudGFyIG5pIHN1cGxhbnRhciBsb3MgZGVyZWNob3MgZGUgYXV0b3IgZGUgdGVyY2Vyb3MsIHkgZGUgdGFsIGZvcm1hLCBlbCBkb2N1bWVudG8gKHRyYWJham8gZGUgZ3JhZG8sIHBhc2FudMOtYSwgY2Fzb3MgbyB0ZXNpcykgZXMgZGUgc3UgZXhjbHVzaXZhIGF1dG9yw61hIHkgdGllbmUgbGEgdGl0dWxhcmlkYWQgc29icmUgw6lzdGUuIFBBUkFHUkFGTzogZW4gY2FzbyBkZSBwcmVzZW50YXJzZSBhbGd1bmEgcmVjbGFtYWNpw7NuIG8gYWNjacOzbiBwb3IgcGFydGUgZGUgdW4gdGVyY2VybywgcmVmZXJlbnRlIGEgbG9zIGRlcmVjaG9zIGRlIGF1dG9yIHNvYnJlIGVsIGRvY3VtZW50byAoVHJhYmFqbyBkZSBncmFkbywgUGFzYW50w61hLCBjYXNvcyBvIHRlc2lzKSBlbiBjdWVzdGnDs24sIEVMIEFVVE9SLCBhc3VtaXLDoSBsYSByZXNwb25zYWJpbGlkYWQgdG90YWwsIHkgc2FsZHLDoSBlbiBkZWZlbnNhIGRlIGxvcyBkZXJlY2hvcyBhcXXDrSBhdXRvcml6YWRvczsgcGFyYSB0b2RvcyBsb3MgZWZlY3RvcywgbGEgVW5pdmVyc2lkYWQgIEF1dMOzbm9tYSBkZSBPY2NpZGVudGUgYWN0w7phIGNvbW8gdW4gdGVyY2VybyBkZSBidWVuYSBmZS4gVG9kYSBwZXJzb25hIHF1ZSBjb25zdWx0ZSB5YSBzZWEgZW4gbGEgYmlibGlvdGVjYSBvIGVuIG1lZGlvIGVsZWN0csOzbmljbyBwb2Ryw6EgY29waWFyIGFwYXJ0ZXMgZGVsIHRleHRvIGNpdGFuZG8gc2llbXByZSBsYSBmdWVudGUsIGVzIGRlY2lyIGVsIHTDrXR1bG8gZGVsIHRyYWJham8geSBlbCBhdXRvci4gRXN0YSBhdXRvcml6YWNpw7NuIG5vIGltcGxpY2EgcmVudW5jaWEgYSBsYSBmYWN1bHRhZCBxdWUgdGllbmUgRUwgQVVUT1IgZGUgcHVibGljYXIgdG90YWwgbyBwYXJjaWFsbWVudGUgbGEgb2JyYS4K |