InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama
Gráficos a color
- Autores:
-
Serrato Echeverry, Carlos Andrés
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2025
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/25109
- Acceso en línea:
- https://hdl.handle.net/10819/25109
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Cáncer de mama
Imágenes médicas
Inteligencia artificial
Modelado 3D
Aprendizaje profundo
Resonancia magnética
Clasificación
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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dc.title.spa.fl_str_mv |
InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama |
title |
InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama |
spellingShingle |
InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Cáncer de mama Imágenes médicas Inteligencia artificial Modelado 3D Aprendizaje profundo Resonancia magnética Clasificación |
title_short |
InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama |
title_full |
InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama |
title_fullStr |
InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama |
title_full_unstemmed |
InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama |
title_sort |
InsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama |
dc.creator.fl_str_mv |
Serrato Echeverry, Carlos Andrés |
dc.contributor.advisor.none.fl_str_mv |
Hidalgo Suárez, Carlos Giovanny |
dc.contributor.author.none.fl_str_mv |
Serrato Echeverry, Carlos Andrés |
dc.contributor.jury.none.fl_str_mv |
Paredes Valencia, Carlos Mario Marin Montealegre, Valeria |
dc.subject.ddc.none.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Cáncer de mama Imágenes médicas Inteligencia artificial Modelado 3D Aprendizaje profundo Resonancia magnética Clasificación |
dc.subject.proposal.spa.fl_str_mv |
Cáncer de mama Imágenes médicas Inteligencia artificial Modelado 3D Aprendizaje profundo Resonancia magnética Clasificación |
description |
Gráficos a color |
publishDate |
2025 |
dc.date.accessioned.none.fl_str_mv |
2025-06-12T15:36:59Z |
dc.date.available.none.fl_str_mv |
2025-06-12T15:36:59Z |
dc.date.issued.none.fl_str_mv |
2025 |
dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
dc.type.content.none.fl_str_mv |
Text |
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info:eu-repo/semantics/bachelorThesis |
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http://purl.org/redcol/resource_type/TP |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
status_str |
acceptedVersion |
dc.identifier.citation.none.fl_str_mv |
C. A. Serrato Echeverry, “Desarrollo de una herramienta basada en modelamiento 3D y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama”, Trabajo de grado, Ingeniería Multimedia e Ingeniería de Sistemas, Universidad de San Buenaventura, Facultad de Ingeniería (Cali), Santiago de Cali, Colombia, 2025. |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10819/25109 |
identifier_str_mv |
C. A. Serrato Echeverry, “Desarrollo de una herramienta basada en modelamiento 3D y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama”, Trabajo de grado, Ingeniería Multimedia e Ingeniería de Sistemas, Universidad de San Buenaventura, Facultad de Ingeniería (Cali), Santiago de Cali, Colombia, 2025. |
url |
https://hdl.handle.net/10819/25109 |
dc.language.iso.none.fl_str_mv |
spa |
language |
spa |
dc.relation.references.none.fl_str_mv |
[1] Organización Mundial de la Salud, “Cáncer de mama,” Organización Mundial de la Salud. Accessed: Mar. 08, 2024. [Online]. Available: https://www.who.int/es/news-room/fact-sheets/detail/breast-cancer [2] Organización Panamericana de la Salud, “Cáncer de mama,” Organización Mundial de la Salud. Accessed: Mar. 08, 2024. [Online]. Available: https://www.paho.org/es/temas/cancer-mama [3] Gobierno de colombia, “Cáncer de mama en Colombia - Cifras,” Instituto de Nacional de Cancerología . Accessed: Mar. 08, 2024. [Online]. Available: https://www.cancer.gov.co/medios-comunicacion-1/infografias/cancer-mama-colombia-cifras [4] Instituto Nacional de Cancerología (INC), “ Anuario estadístico 2022,” Bogotá, D. C., 2023. Accessed: Apr. 22, 2024. [Online]. Available: https://www.cancer.gov.co/conozca-sobre-cancer-1/publicaciones/anuario-estadistico-2022 [5] Ó. A. Bonilla-Sepúlveda, “Efectividad de la mamografía como prueba de tamizaje para reducir la mortalidad por cáncer de mama: revisión sistemática,” Tecnología Medicina & Laboratorio, vol. 20, 2018. [6] O. Díaz, A. Rodríguez-Ruiz, A. Gubern-Mérida, R. Martí, and M. Chevalier, “¿Son los sistemas de inteligencia artificial una herramienta útil para los programas de cribado de cáncer de mama?,” Radiologia, vol. 63, no. 3, pp. 236–244, May 2021, doi: 10.1016/j.rx.2020.11.006. [7] densebreast-info, “¿Qué es la densidad mamaria y por qué es importante?” Accessed: Mar. 27, 2024. [Online]. Available: https://densebreast-info.org/spanish_faq_category/que-es-la-densidad-mamaria-y-por-que-es-importante/ [8] D. D, L. D, C. K, M. Y, and S.-L. M, “Standard and Delayed Contrast-Enhanced MRI of Malignant and Benign Breast Lesions with Histological and Clinical Supporting Data (Advanced-MRI-Breast-Lesions) (Version 2) ,” 2024. doi: https://doi.org/10.7937/C7X1-YN57. [9] H. E. Kim et al., “Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study,” Lancet Digit Health, vol. 2, no. 3, 2020, doi: 10.1016/S2589-7500(20)30003-0. [10] T. Q. H. Ho et al., “Cumulative Probability of False-Positive Results after 10 Years of Screening with Digital Breast Tomosynthesis vs Digital Mammography,” JAMA Netw Open, vol. 5, no. 3, 2022, doi: 10.1001/jamanetworkopen.2022.2440. [11] I. Pérez-Zúñiga, Y. Villaseñor-Navarro, M. P. Pérez-Badillo, R. Cruz-Morales, C. Pavón-Hernández, and L. Aguilar-Cortázar, “Gaceta Mexicana de oncología,” Gaceta Mexicana de Oncología, vol. 11, no. 4, pp. 268–280, Jul. 2002, Accessed: Oct. 06, 2024. [Online]. Available: http://www.elsevier.es/es-revista-gaceta-mexicana-oncologia-305-articulo-resonancia-magnetica-mama-sus-aplicaciones-X1665920112544919 [12] C. Erolin, “Interactive 3D Digital Models for Anatomy and Medical Education,” in Advances in Experimental Medicine and Biology, vol. 1138, 2019. doi: 10.1007/978-3-030-14227-8_1. [13] Ministerio de Salud y Protección Social, “ Detección temprana del cáncer de mama disminuye en un 25 % probabilidad de morir por esta causa.” Accessed: Mar. 08, 2024. [Online]. Available: https://www.minsalud.gov.co/Paginas/Deteccion-temprana-del-cancer-de-mama.aspx [14] D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement,” PLoS Med, vol. 6, no. 7, p. e1000097, Jul. 2009, doi: 10.1371/journal.pmed.1000097. [15] R. Khaled, J. Vidal, J. C. Vilanova, and R. Martí, “A U-Net Ensemble for breast lesion segmentation in DCE MRI,” Comput Biol Med, vol. 140, Jan. 2022, doi: 10.1016/j.compbiomed.2021.105093. [16] W. Hao, J. Gong, S. Wang, H. Zhu, B. Zhao, and W. Peng, “Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment,” Front Oncol, vol. 10, Oct. 2020, doi: 10.3389/fonc.2020.531476. [17] T. P. B. Gamage et al., “An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment,” Interface Focus, vol. 9, no. 4, Aug. 2019, doi: 10.1098/rsfs.2019.0034. [18] A. Vamvakas, D. Tsivaka, A. Logothetis, K. Vassiou, and I. Tsougos, “Breast Cancer Classification on Multiparametric MRI – Increased Performance of Boosting Ensemble Methods,” Technol Cancer Res Treat, vol. 21, Mar. 2022, doi: 10.1177/15330338221087828. [19] M. Ma et al., “Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer,” Comput Math Methods Med, vol. 2021, 2021, doi: 10.1155/2021/2140465. [20] E. Tagliabue, D. Dall’Alba, E. Magnabosco, C. Tenga, I. Peterlik, and P. Fiorini, “Position-based modeling of lesion displacement in ultrasound-guided breast biopsy,” Int J Comput Assist Radiol Surg, vol. 14, no. 8, pp. 1329–1339, Aug. 2019, doi: 10.1007/s11548-019-01997-z. [21] J. Zhou et al., “BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning,” Front Oncol, vol. 11, Nov. 2021, doi: 10.3389/fonc.2021.728224. [22] W. Sheng et al., “Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning,” Front Oncol, vol. 12, Sep. 2022, doi: 10.3389/fonc.2022.964605. [23] H. Huo, H. Deng, J. Gao, H. Duan, and C. Ma, “Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom,” Sensors, vol. 23, no. 15, Aug. 2023, doi: 10.3390/s23156970. [24] G. Zheng et al., “Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms,” J Cancer Res Clin Oncol, vol. 150, no. 3, Mar. 2024, doi: 10.1007/s00432-024-05680-y. [25] K. A. Chen et al., “Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status,” Ann Surg Oncol, vol. 30, no. 12, pp. 7107–7115, Nov. 2023, doi: 10.1245/s10434-023-14083-1. [26] S. Park, U. Villa, F. Li, R. M. Cam, A. A. Oraevsky, and M. A. Anastasio, “Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer,” J Biomed Opt, vol. 28, no. 06, Jun. 2023, doi: 10.1117/1.jbo.28.6.066002. [27] S. S. Chowa, S. Azam, S. Montaha, M. R. I. Bhuiyan, and M. Jonkman, “Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network,” Journal of Imaging Informatics in Medicine, vol. 37, no. 3, pp. 1067–1085, Feb. 2024, doi: 10.1007/s10278-024-00983-5. [28] B. Dołęga-Kozierowski et al., “Numerical and physical modeling of breast cancer based on image fusion and artificial intelligence,” Breast Cancer Res Treat, vol. 202, no. 1, pp. 33–43, Nov. 2023, doi: 10.1007/s10549-023-07056-1. [29] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Dec. 2015, [Online]. Available: http://arxiv.org/abs/1512.03385 [30] Z. Wang, J. Zhang, and N. Verma, “Realizing Low-Energy Classification Systems by Implementing Matrix Multiplication Directly Within an ADC,” IEEE Trans Biomed Circuits Syst, pp. 1–1, 2015, doi: 10.1109/TBCAS.2015.2500101. [31] S. K. Parhi and S. K. Patro, “Compressive strength prediction of PET fiber-reinforced concrete using Dolphin echolocation optimized decision tree-based machine learning algorithms,” Asian Journal of Civil Engineering, vol. 25, no. 1, pp. 977–996, Jan. 2024, doi: 10.1007/s42107-023-00826-8. [32] L. M. Demajo, “Explainable AI for Interpretable Credit Scoring,” Aug. 2020. [Online]. Available: https://www.researchgate.net/publication/350874464 [33] H.-T. Thai, “Machine learning for structural engineering: A state-of-the-art review,” Structures, vol. 38, pp. 448–491, Apr. 2022, doi: 10.1016/j.istruc.2022.02.003. [34] IMB, “¿Qué es el análisis discriminante lineal (LDA)?,” IMB. Accessed: May 29, 2025. [Online]. Available: https://www.ibm.com/es-es/think/topics/linear-discriminant-analysis [35] Data Base Camp, “What is the Naive Bayes Algorithm?,” Data Base Camp. Accessed: May 29, 2025. [Online]. Available: https://databasecamp.de/en/ml/naive-bayes-algorithm [36] M.-H. Chiu and L. C. Hao, THE USE OF FACIAL MICRO-EXPRESSION STATE AND TREE-FOREST MODEL FOR PREDICTING CONCEPTUAL-CONFLICT BASED CONCEPTUAL CHANGE. 2016. [Online]. Available: https://www.researchgate.net/publication/295860754 [37] W. Zhang, “Machine Learning Approaches to Predicting Company Bankruptcy,” Journal of Financial Risk Management, vol. 06, no. 04, pp. 364–374, 2017, doi: 10.4236/jfrm.2017.64026. [38] M. Y. Khan, A. Qayoom, M. S. Nizami, M. S. Siddiqui, S. Wasi, and S. M. K.-R. Raazi, “Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding‐Based Deep Learning Techniques,” Complexity, vol. 2021, no. 1, Jan. 2021, doi: 10.1155/2021/2553199. [39] Natassha Selvaraj, “Logistic Regression Explained in 7 Minutes,” Medium. Accessed: May 29, 2025. [Online]. Available: https://medium.com/data-science/logistic-regression-explained-in-7-minutes-f648bf44d53e [40] Gabriel Costa, “A first insight into Bayesian Neural Networks (BNNs),” Medium. Accessed: May 29, 2025. [Online]. Available: https://medium.com/@costaleirbag/a-first-insight-into-bayesian-neural-networks-bnn-c767551e9526 [41] M. Zhang, P. Kadam, H. You, S. Liu, and C.-C. J. Kuo, “PointHop: An Explainable Machine Learning Method for Point Cloud Classification”, doi: 10.48550/arXiv.1907.12766. [42] Z. Long and K. Nagamune, “Zhongjie Long, Kouki Nagamune. A Marching Cubes Algorithm: Application for Three-dimensional Surface Reconstruction Based on Endoscope and Optical Fiber. Information, International Information Institute,” 2015. [Online]. Available: https://hal.inria.fr/hal-01205823 [43] Ebrahim Pichka, “Graph Attention Networks Paper Explained With Illustration and PyTorch Implementation,” Ebrahim Pichka. Accessed: May 29, 2025. [Online]. Available: https://epichka.com/blog/2023/gat-paper-explained/ [44] Y. Ding, F. Chen, Y. Zhao, Z. Wu, C. Zhang, and D. Wu, “A Stacked Multi-Connection Simple Reducing Net for Brain Tumor Segmentation,” IEEE Access, vol. 7, pp. 104011–104024, 2019, doi: 10.1109/ACCESS.2019.2926448. [45] Aditi Rastogi, “ResNet50,” Medium. Accessed: May 29, 2025. [Online]. Available: https://blog.devgenius.io/resnet50-6b42934db431 [46] M. M. Hussain, P. Shanmugam, K. Moorthi, U. Sakthivelu, A. Rajasekar, and R. N. Kumar, “An Ensemble Deep Learning Model for Diabetic Retinopathy Identification,” in 2023 9th International Conference on Smart Structures and Systems (ICSSS), IEEE, Nov. 2023, pp. 1–7. doi: 10.1109/ICSSS58085.2023.10407073. [47] Siladittya Manna, “Building Inception-Resnet-V2 in Keras from scratch,” Medium. Accessed: May 29, 2025. [Online]. Available: https://medium.com/the-owl/building-inception-resnet-v2-in-keras-from-scratch-a3546c4d93f0 [48] Shuvam Das Shuvam Das Follow Shuvam Das 12 followers Machine Learning Enthusiast., “Implementing DenseNet-121 in PyTorch: A Step-by-Step Guide,” Medium. Accessed: May 29, 2025. [Online]. Available: https://medium.com/deepkapha-notes/implementing-densenet-121-in-pytorch-a-step-by-step-guide-c0c2625c2a60 [49] L. Xiang, Y. Li, W. Lin, Q. Wang, and D. Shen, “Unpaired Deep Cross-Modality Synthesis with Fast Training,” 2018, pp. 155–164. doi: 10.1007/978-3-030-00889-5_18. [50] Virender Singh, “Crystal Method in Agile.” Accessed: Sep. 12, 2024. [Online]. Available: https://www.toolsqa.com/agile/crystal-method/ [51] A. Cockburn, Crystal clear a human-powered methodology for small teams. 2004. [52] IBM, “Conceptos básicos de ayuda de CRISP-DM,” IBM. Accessed: Nov. 30, 2024. [Online]. Available: https://www.ibm.com/docs/es/spss-modeler/saas?topic=dm-crisp-help-overview [53] scar Marbn, G. Mariscal, and J. Segovi, “A Data Mining & Knowledge Discovery Process Model,” in Data Mining and Knowledge Discovery in Real Life Applications, I-Tech Education and Publishing, 2009. doi: 10.5772/6438. |
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Hidalgo Suárez, Carlos Giovannyvirtual::4014-1Serrato Echeverry, Carlos AndrésParedes Valencia, Carlos Mariovirtual::4016-1Marin Montealegre, Valeria2025-06-12T15:36:59Z2025-06-12T15:36:59Z2025Gráficos a colorEl cáncer de mama es el tipo de cáncer más común en mujeres a nivel mundial y constituye una de las principales causas de mortalidad en este grupo poblacional en Colombia. A pesar de los avances en diagnóstico por imágenes, persisten limitaciones significativas que generan falsos positivos y negativos, especialmente en pacientes con alta densidad mamaria, lo que afecta la precisión en la detección temprana y subraya la necesidad de desarrollar tecnologías que mejoren la interpretación de imágenes médicas. En respuesta a este desafío, este trabajo presenta el desarrollo de InsightPrism, una herramienta computacional orientada al análisis del cáncer de mama, que integra modelamiento 3D e inteligencia artificial para facilitar la visualización e interpretación de imágenes médicas en formato DICOM (Digital communication in Medicine).Breast cancer is the most common type of cancer among women worldwide and is one of the leading causes of mortality in this population group in Colombia. Despite advances in imaging diagnosis, significant limitations remain that generate false positives and negatives, especially in patients with high breast density, which affects the accuracy of early detection and highlights the need to develop technologies that improve the interpretation of medical images. In response to this challenge, this work presents the development of InsightPrism, a computational tool aimed at breast cancer analysis that integrates 3D modeling and artificial intelligence to facilitate the visualization and interpretation of medical images in DICOM format.PregradoIngeniero MultimediaIngeniero de Sistemas124 páginasapplication/pdfC. A. Serrato Echeverry, “Desarrollo de una herramienta basada en modelamiento 3D y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mama”, Trabajo de grado, Ingeniería Multimedia e Ingeniería de Sistemas, Universidad de San Buenaventura, Facultad de Ingeniería (Cali), Santiago de Cali, Colombia, 2025.https://hdl.handle.net/10819/25109spaUniversidad de San Buenaventura - CaliCaliFacultad de IngenieríaCaliIngeniería MultimediaIngeniería de Sistemas[1] Organización Mundial de la Salud, “Cáncer de mama,” Organización Mundial de la Salud. Accessed: Mar. 08, 2024. [Online]. Available: https://www.who.int/es/news-room/fact-sheets/detail/breast-cancer[2] Organización Panamericana de la Salud, “Cáncer de mama,” Organización Mundial de la Salud. Accessed: Mar. 08, 2024. [Online]. Available: https://www.paho.org/es/temas/cancer-mama[3] Gobierno de colombia, “Cáncer de mama en Colombia - Cifras,” Instituto de Nacional de Cancerología . Accessed: Mar. 08, 2024. [Online]. Available: https://www.cancer.gov.co/medios-comunicacion-1/infografias/cancer-mama-colombia-cifras[4] Instituto Nacional de Cancerología (INC), “ Anuario estadístico 2022,” Bogotá, D. C., 2023. Accessed: Apr. 22, 2024. [Online]. Available: https://www.cancer.gov.co/conozca-sobre-cancer-1/publicaciones/anuario-estadistico-2022[5] Ó. A. Bonilla-Sepúlveda, “Efectividad de la mamografía como prueba de tamizaje para reducir la mortalidad por cáncer de mama: revisión sistemática,” Tecnología Medicina & Laboratorio, vol. 20, 2018.[6] O. Díaz, A. Rodríguez-Ruiz, A. Gubern-Mérida, R. Martí, and M. Chevalier, “¿Son los sistemas de inteligencia artificial una herramienta útil para los programas de cribado de cáncer de mama?,” Radiologia, vol. 63, no. 3, pp. 236–244, May 2021, doi: 10.1016/j.rx.2020.11.006.[7] densebreast-info, “¿Qué es la densidad mamaria y por qué es importante?” Accessed: Mar. 27, 2024. [Online]. Available: https://densebreast-info.org/spanish_faq_category/que-es-la-densidad-mamaria-y-por-que-es-importante/[8] D. D, L. D, C. K, M. Y, and S.-L. M, “Standard and Delayed Contrast-Enhanced MRI of Malignant and Benign Breast Lesions with Histological and Clinical Supporting Data (Advanced-MRI-Breast-Lesions) (Version 2) ,” 2024. doi: https://doi.org/10.7937/C7X1-YN57.[9] H. E. Kim et al., “Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study,” Lancet Digit Health, vol. 2, no. 3, 2020, doi: 10.1016/S2589-7500(20)30003-0.[10] T. Q. H. Ho et al., “Cumulative Probability of False-Positive Results after 10 Years of Screening with Digital Breast Tomosynthesis vs Digital Mammography,” JAMA Netw Open, vol. 5, no. 3, 2022, doi: 10.1001/jamanetworkopen.2022.2440.[11] I. Pérez-Zúñiga, Y. Villaseñor-Navarro, M. P. Pérez-Badillo, R. Cruz-Morales, C. Pavón-Hernández, and L. Aguilar-Cortázar, “Gaceta Mexicana de oncología,” Gaceta Mexicana de Oncología, vol. 11, no. 4, pp. 268–280, Jul. 2002, Accessed: Oct. 06, 2024. [Online]. Available: http://www.elsevier.es/es-revista-gaceta-mexicana-oncologia-305-articulo-resonancia-magnetica-mama-sus-aplicaciones-X1665920112544919[12] C. Erolin, “Interactive 3D Digital Models for Anatomy and Medical Education,” in Advances in Experimental Medicine and Biology, vol. 1138, 2019. doi: 10.1007/978-3-030-14227-8_1.[13] Ministerio de Salud y Protección Social, “ Detección temprana del cáncer de mama disminuye en un 25 % probabilidad de morir por esta causa.” Accessed: Mar. 08, 2024. [Online]. Available: https://www.minsalud.gov.co/Paginas/Deteccion-temprana-del-cancer-de-mama.aspx[14] D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement,” PLoS Med, vol. 6, no. 7, p. e1000097, Jul. 2009, doi: 10.1371/journal.pmed.1000097.[15] R. Khaled, J. Vidal, J. C. Vilanova, and R. Martí, “A U-Net Ensemble for breast lesion segmentation in DCE MRI,” Comput Biol Med, vol. 140, Jan. 2022, doi: 10.1016/j.compbiomed.2021.105093.[16] W. Hao, J. Gong, S. Wang, H. Zhu, B. Zhao, and W. Peng, “Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment,” Front Oncol, vol. 10, Oct. 2020, doi: 10.3389/fonc.2020.531476.[17] T. P. B. Gamage et al., “An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment,” Interface Focus, vol. 9, no. 4, Aug. 2019, doi: 10.1098/rsfs.2019.0034.[18] A. Vamvakas, D. Tsivaka, A. Logothetis, K. Vassiou, and I. Tsougos, “Breast Cancer Classification on Multiparametric MRI – Increased Performance of Boosting Ensemble Methods,” Technol Cancer Res Treat, vol. 21, Mar. 2022, doi: 10.1177/15330338221087828.[19] M. Ma et al., “Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer,” Comput Math Methods Med, vol. 2021, 2021, doi: 10.1155/2021/2140465.[20] E. Tagliabue, D. Dall’Alba, E. Magnabosco, C. Tenga, I. Peterlik, and P. Fiorini, “Position-based modeling of lesion displacement in ultrasound-guided breast biopsy,” Int J Comput Assist Radiol Surg, vol. 14, no. 8, pp. 1329–1339, Aug. 2019, doi: 10.1007/s11548-019-01997-z.[21] J. Zhou et al., “BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning,” Front Oncol, vol. 11, Nov. 2021, doi: 10.3389/fonc.2021.728224.[22] W. Sheng et al., “Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning,” Front Oncol, vol. 12, Sep. 2022, doi: 10.3389/fonc.2022.964605.[23] H. Huo, H. Deng, J. Gao, H. Duan, and C. Ma, “Mitigating Under-Sampling Artifacts in 3D Photoacoustic Imaging Using Res-UNet Based on Digital Breast Phantom,” Sensors, vol. 23, no. 15, Aug. 2023, doi: 10.3390/s23156970.[24] G. Zheng et al., “Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms,” J Cancer Res Clin Oncol, vol. 150, no. 3, Mar. 2024, doi: 10.1007/s00432-024-05680-y.[25] K. A. Chen et al., “Analysis of Specimen Mammography with Artificial Intelligence to Predict Margin Status,” Ann Surg Oncol, vol. 30, no. 12, pp. 7107–7115, Nov. 2023, doi: 10.1245/s10434-023-14083-1.[26] S. Park, U. Villa, F. Li, R. M. Cam, A. A. Oraevsky, and M. A. Anastasio, “Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer,” J Biomed Opt, vol. 28, no. 06, Jun. 2023, doi: 10.1117/1.jbo.28.6.066002.[27] S. S. Chowa, S. Azam, S. Montaha, M. R. I. Bhuiyan, and M. Jonkman, “Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network,” Journal of Imaging Informatics in Medicine, vol. 37, no. 3, pp. 1067–1085, Feb. 2024, doi: 10.1007/s10278-024-00983-5.[28] B. Dołęga-Kozierowski et al., “Numerical and physical modeling of breast cancer based on image fusion and artificial intelligence,” Breast Cancer Res Treat, vol. 202, no. 1, pp. 33–43, Nov. 2023, doi: 10.1007/s10549-023-07056-1.[29] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Dec. 2015, [Online]. Available: http://arxiv.org/abs/1512.03385[30] Z. Wang, J. Zhang, and N. Verma, “Realizing Low-Energy Classification Systems by Implementing Matrix Multiplication Directly Within an ADC,” IEEE Trans Biomed Circuits Syst, pp. 1–1, 2015, doi: 10.1109/TBCAS.2015.2500101.[31] S. K. Parhi and S. K. Patro, “Compressive strength prediction of PET fiber-reinforced concrete using Dolphin echolocation optimized decision tree-based machine learning algorithms,” Asian Journal of Civil Engineering, vol. 25, no. 1, pp. 977–996, Jan. 2024, doi: 10.1007/s42107-023-00826-8.[32] L. M. Demajo, “Explainable AI for Interpretable Credit Scoring,” Aug. 2020. [Online]. Available: https://www.researchgate.net/publication/350874464[33] H.-T. Thai, “Machine learning for structural engineering: A state-of-the-art review,” Structures, vol. 38, pp. 448–491, Apr. 2022, doi: 10.1016/j.istruc.2022.02.003.[34] IMB, “¿Qué es el análisis discriminante lineal (LDA)?,” IMB. Accessed: May 29, 2025. [Online]. Available: https://www.ibm.com/es-es/think/topics/linear-discriminant-analysis[35] Data Base Camp, “What is the Naive Bayes Algorithm?,” Data Base Camp. Accessed: May 29, 2025. [Online]. Available: https://databasecamp.de/en/ml/naive-bayes-algorithm[36] M.-H. Chiu and L. C. Hao, THE USE OF FACIAL MICRO-EXPRESSION STATE AND TREE-FOREST MODEL FOR PREDICTING CONCEPTUAL-CONFLICT BASED CONCEPTUAL CHANGE. 2016. [Online]. Available: https://www.researchgate.net/publication/295860754[37] W. Zhang, “Machine Learning Approaches to Predicting Company Bankruptcy,” Journal of Financial Risk Management, vol. 06, no. 04, pp. 364–374, 2017, doi: 10.4236/jfrm.2017.64026.[38] M. Y. Khan, A. Qayoom, M. S. Nizami, M. S. Siddiqui, S. Wasi, and S. M. K.-R. Raazi, “Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding‐Based Deep Learning Techniques,” Complexity, vol. 2021, no. 1, Jan. 2021, doi: 10.1155/2021/2553199.[39] Natassha Selvaraj, “Logistic Regression Explained in 7 Minutes,” Medium. Accessed: May 29, 2025. [Online]. Available: https://medium.com/data-science/logistic-regression-explained-in-7-minutes-f648bf44d53e[40] Gabriel Costa, “A first insight into Bayesian Neural Networks (BNNs),” Medium. Accessed: May 29, 2025. [Online]. Available: https://medium.com/@costaleirbag/a-first-insight-into-bayesian-neural-networks-bnn-c767551e9526[41] M. Zhang, P. Kadam, H. You, S. Liu, and C.-C. J. Kuo, “PointHop: An Explainable Machine Learning Method for Point Cloud Classification”, doi: 10.48550/arXiv.1907.12766.[42] Z. Long and K. Nagamune, “Zhongjie Long, Kouki Nagamune. A Marching Cubes Algorithm: Application for Three-dimensional Surface Reconstruction Based on Endoscope and Optical Fiber. Information, International Information Institute,” 2015. [Online]. Available: https://hal.inria.fr/hal-01205823[43] Ebrahim Pichka, “Graph Attention Networks Paper Explained With Illustration and PyTorch Implementation,” Ebrahim Pichka. Accessed: May 29, 2025. [Online]. Available: https://epichka.com/blog/2023/gat-paper-explained/[44] Y. Ding, F. Chen, Y. Zhao, Z. Wu, C. Zhang, and D. Wu, “A Stacked Multi-Connection Simple Reducing Net for Brain Tumor Segmentation,” IEEE Access, vol. 7, pp. 104011–104024, 2019, doi: 10.1109/ACCESS.2019.2926448.[45] Aditi Rastogi, “ResNet50,” Medium. Accessed: May 29, 2025. [Online]. Available: https://blog.devgenius.io/resnet50-6b42934db431[46] M. M. Hussain, P. Shanmugam, K. Moorthi, U. Sakthivelu, A. Rajasekar, and R. N. Kumar, “An Ensemble Deep Learning Model for Diabetic Retinopathy Identification,” in 2023 9th International Conference on Smart Structures and Systems (ICSSS), IEEE, Nov. 2023, pp. 1–7. doi: 10.1109/ICSSS58085.2023.10407073.[47] Siladittya Manna, “Building Inception-Resnet-V2 in Keras from scratch,” Medium. Accessed: May 29, 2025. [Online]. Available: https://medium.com/the-owl/building-inception-resnet-v2-in-keras-from-scratch-a3546c4d93f0[48] Shuvam Das Shuvam Das Follow Shuvam Das 12 followers Machine Learning Enthusiast., “Implementing DenseNet-121 in PyTorch: A Step-by-Step Guide,” Medium. Accessed: May 29, 2025. [Online]. Available: https://medium.com/deepkapha-notes/implementing-densenet-121-in-pytorch-a-step-by-step-guide-c0c2625c2a60[49] L. Xiang, Y. Li, W. Lin, Q. Wang, and D. Shen, “Unpaired Deep Cross-Modality Synthesis with Fast Training,” 2018, pp. 155–164. doi: 10.1007/978-3-030-00889-5_18.[50] Virender Singh, “Crystal Method in Agile.” Accessed: Sep. 12, 2024. [Online]. Available: https://www.toolsqa.com/agile/crystal-method/[51] A. Cockburn, Crystal clear a human-powered methodology for small teams. 2004.[52] IBM, “Conceptos básicos de ayuda de CRISP-DM,” IBM. Accessed: Nov. 30, 2024. [Online]. Available: https://www.ibm.com/docs/es/spss-modeler/saas?topic=dm-crisp-help-overview[53] scar Marbn, G. Mariscal, and J. Segovi, “A Data Mining & Knowledge Discovery Process Model,” in Data Mining and Knowledge Discovery in Real Life Applications, I-Tech Education and Publishing, 2009. doi: 10.5772/6438.info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Cali000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresCáncer de mamaImágenes médicasInteligencia artificialModelado 3DAprendizaje profundoResonancia magnéticaClasificaciónInsightPrism: una herramienta basada en modelamiento 3d y técnicas computacionales de inteligencia artificial para el estudio y análisis del cáncer de mamaTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttp://purl.org/redcol/resource_type/TPinfo:eu-repo/semantics/acceptedVersionComunidad Científica y AcadémicaPublicationhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001575691virtual::4014-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000010211virtual::4016-1https://scholar.google.com/citations?user=4QfTUFYAAAAJ&hl=esvirtual::4014-10000-0003-2308-0720virtual::4014-10000-0002-8951-5259virtual::4016-119e1d8c4-f4bf-4b9a-bea4-330895ce7b4evirtual::4014-119e1d8c4-f4bf-4b9a-bea4-330895ce7b4evirtual::4014-1132e099c-e943-429f-b4b5-3e80792e5cacvirtual::4016-1132e099c-e943-429f-b4b5-3e80792e5cacvirtual::4016-1ORIGINALIA_3D_Cancer_Serrato_2025.pdfIA_3D_Cancer_Serrato_2025.pdfapplication/pdf3956581https://bibliotecadigital.usb.edu.co/bitstreams/43966aaf-c063-4167-863b-af9c2142c360/downloadf39c94b4db32631e924c6079feaf8e6bMD51Formato_Autorizacion_Publicacion_Repositorio_USBCol.pdfFormato_Autorizacion_Publicacion_Repositorio_USBCol.pdfapplication/pdf225014https://bibliotecadigital.usb.edu.co/bitstreams/3877fd09-c645-4666-84da-375b35a39030/download3c91e0ca444e26084c7708f55a70a7a2MD52LICENSElicense.txtlicense.txttext/plain; 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