Prototipo de un sistema de diagnóstico asistido por ordenador orientado a la localización de clusters de microcalcificaciones en mamografías

dc.contributorOrozco Gutiérrez , Álvaro Ángel
dc.contributorGrupo de Investigación en Automática Pereira - Risaralda
dc.creatorHernández Gómez, Kevin Alejandro
dc.date2022-09-09T21:37:53Z
dc.date2022-09-09T21:37:53Z
dc.date2022
dc.date.accessioned2022-09-23T21:23:41Z
dc.date.available2022-09-23T21:23:41Z
dc.identifierUniversidad Tecnológica de Pereira
dc.identifierRepositorio Institucional Universidad Tecnológica de Pereira
dc.identifierhttps://repositorio.utp.edu.co/home
dc.identifierhttps://hdl.handle.net/11059/14254
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3529246
dc.descriptionEn este trabajo se presenta la construcción metodológica para la implementación de un prototipo de aplicativo software que sirva como herramienta de apoyo al diagnóstico de cáncer de mama, a partir de las diferentes técnicas de procesamiento de imágenes y modelos de aprendizaje supervisado y no-supervisado. Tiene como aporte fundamental el hecho de que es una metodología que acopla diferentes etapas de procesamiento bastante robustas que permiten hacer un tratamiento desde la imagen mamográfica en crudo hasta la recomendación final dada por el sistema (End-to-End). En particular se consideró la técnica de realce de contraste de corrección gamma adaptativa con ponderación distribuida (AGCWD) y binarización de Otsu para la segmentación del tejido mamario, el segmentador K-means para la identificación del musculo pectoral, una red neuronal convolucional (CNN) para la localización de microcalcificaciones, un ensamble de redes neuronales artificiales (RNA) responsable de la clasificación y del proceso de búsqueda de imágenes similares. Además, se usó la librería tkinter para la implementación de la interfaz gráfica de usuario (GUI) en Python. Para la validación de la metodología se usaron dos bases de datos, The Mammographic Image Analysis (mini-MIAS) y The Digital Database for Screening Mammography (DDSM). Image Analysis (mini-MIAS) y The Digital Database for Screening Mammography (DDSM). Los resultados obtenidos reflejan que esta metodología mejora sustancialmente el rendimiento en la eliminación de artefactos (99.78%), la precisión en la remoción del musculo pectoral (92.14%), la reducción de falsos positivos en la detección de microcalcificaciones (0.47 por imagen), y aumento en el acierto en la clasificación según el estándar BI-RADS (82%) en comparación a otros trabajos en el estado del arte.
dc.descriptionThis work presents the methodological construction for the implementation of a prototype software application that serves as a support tool for breast cancer diagnosis, based on different image processing techniques and supervised and unsupervised learning models. Its fundamental contribution is the fact that it is a methodology that couples different processing stages quite robust that allow a treatment from the raw mammographic image to the final recommendation given by the system (End-to-End). In particular, the contrast enhancement with adaptive gamma correction weighting distribution (AGCWD) and Otsu binarization technique was considered for the segmentation of breast tissue, the K-means segmenter for the identification of pectoral muscle, a convolutional neural network (CNN) for the localization of microcalcifications, an assembly of artificial neural networks (ANN) responsible for the classification and the search process of similar images. In addition, the tkinter library was used for the implementation of the graphical user interface (GUI) in Python. Two databases, The Mammographic Image Analysis (mini-MIAS) and The Digital Database for Screening Mammography (DDSM), were used to validate the methodology. The results obtained reflect that this methodology substantially improves the performance in the elimination of artifacts (99.78%), the accuracy in the removal of the pectoral muscle (92.14%), the reduction of false positives in the detection of microcalcifications (0.47 per image), and the increase in the accuracy in the classification according to the BI RADS standard (82%) in comparison to other works in the state of the art
dc.descriptionMaestría
dc.descriptionMagíster en Ingeniería Eléctrica
dc.descriptionIndice 1. Resumen 1 2. Abstract 2 3. Introducción 3 4. Planteamiento del problema 4 5. Justificación 8 6. Objetivos 9 6.1. General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 6.2. Específicos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 7. Revision del estado del arte 10 7.1. Eliminacion de artefactos y remoción del musculo pectoral . . . . . . 10 7.2. Deteccion de clusters de microcalcificaciones . . . . . . . . . . . . . . 11 7.3. Clasificación de clusters de microcalcificaciones . . . . . . . . . . . . . 12 8. Marco Teórico 14 8.1. Eliminacion de ruido y supresion de artefactos . . . . . . . . . . . . . 15 8.1.1. Realce de contraste por corrección gamma adaptativa con ponderación distribuida . . . . . . 15 8.1.2. Segmentación del tejido mamario . . . . . . . . . . . . . . . . 16 8.2. Remocion del musculo pectoral . . . . . . . . . . . . . . . . . . . . . 18 8.2.1. Segmentacion del musculo pectoral con K-medias . . . . . . . 19 8.2.2. Correccion del contorno con aproximación polinomial . . . . . 20 8.3. Deteccion y localizaci´on de microcalcificaciones . . . . . . . . . . . . 22 8.3.1. Deteccion de MC con CNN . . . . . . . . . . . . . . . . . . . 22 8.3.2. Realce de contraste, segmentación y filtrado de MC . . . . . . 24 8.4. Clasificación de microcalcificaciones según su categoría BI-RADS . . 26 8.4.1. Escala BI-RADS . . . . . . . . . . . . . . . . . . . . . . . . . 26 8.4.2. Extracci´on de características . . . . . . . . . . . . . . . . . . . 27 8.4.3. Redes Neuronales Artificiales . . . . . . . . . . . . . . . . . . 27 8.4.4. Sistema de recuperación de imágenes de microcalcificaciones . 28 9. Marco experimental 30 9.1. Bases de datos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 9.2. Resultados de las pruebas de eliminación de artefactos . . . . . . . . 31 9.3. Resultados de las pruebas de remoción del musculo pectoral . . . . . 32 9.4. Resultados de las pruebas de detección y localización de MC . . . . . 34 9.5. Resultados de las pruebas de clasificacion de MC según su categoría BI-RADS . . . . . . 36 9.6. Diseño de la interfaz del sistema DAO . . . . . . . . . . . . . . . . . 37 10.Conclusiones 41 11.Resultados académicos 44 12.Agradecimientos 45 13.Bibliografía 46
dc.format57 Páginas
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dc.formatapplication/pdf
dc.languagespa
dc.publisherUniversidad Tecnológica de Pereira
dc.publisherFacultad de Ingenierías
dc.publisherPereira
dc.publisherMaestría en Ingeniería Eléctrica
dc.relationH. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: a cancer journal for clinicians, vol. 71, no. 3, pp. 209–249, 2021
dc.relationX. Li, H. Gao, Z. Chen, L. Zhang, X. Zhu, S. Wang, and W. Peng, “Diagnosis of breast cancer based on microcalcifications using grating-based phase contrast ct,” European radiology, vol. 28, no. 9, pp. 3742–3750, 2018
dc.relationJ. Hern´andez-Capistr´an, J. F. Mart´ınez-Carballido, and R. Rosas-Romero, “False positive reduction by an annular model as a set of few features for microcalci fication detection to assist early diagnosis of breast cancer,” Journal of medical systems, vol. 42, no. 8, p. 134, 2018
dc.relationM. Moghbel, C. Y. Ooi, N. Ismail, Y. W. Hau, and N. Memari, “A review of breast boundary and pectoral muscle segmentation methods in computer-aided detection/diagnosis of breast mammography,” Artificial Intelligence Review, pp. 1–46, 2019.
dc.relationT. Basile, A. Fanizzi, L. Losurdo, R. Bellotti, U. Bottigli, R. Dentamaro, V. Di donna, A. Fausto, R. Massafra, M. Moschetta et al., “Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system,” Physica Medica, vol. 64, pp. 1–9, 2019
dc.relationBreastcancer.org, “Diagnosis of dcis,” https://www.breastcancer.org/ symptoms/types/dcis/diagnosis, 2018, Accessed : 2020 − 04 − 25.
dc.relationA. C. S. (ACS), “What is breast cancer?” https://www.cancer.org/cancer/ breast-cancer/about/what-is-breast-cancer.html, 2019, Accessed : 2020−04−23
dc.relationM. El-Shenawee, “Electromagnetic imaging for breast cancer research,” in 2011 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems. IEEE, 2011, pp. 55–58
dc.relationA. M. Hassan and M. El-Shenawee, “Review of electromagnetic techniques for breast cancer detection,” IEEE Reviews in Biomedical Engineering, vol. 4, pp. 103–118, 2011.
dc.relationW. H. O. (WHO), “Breast cancer,” https://www.who.int/cancer/prevention/ diagnosis-screening/breast-cancer/en/, 2020, Accessed : 2020 − 04 − 25.
dc.relationV. A. Karale, T. Singh, A. Sadhu, N. Khandelwal, and S. Mukhopadhyay, “Re duction of false positives in the screening cad tool for microcalcification detec tion,” S¯adhan¯a, vol. 45, no. 1, p. 44, 2020
dc.relationC. D’Orsi, L. Bassett, S. Feig et al., “Breast imaging reporting and data system (bi-rads),” Breast imaging atlas, 4th edn. American College of Radiology, Reston, 2018.
dc.relationS. Rahimeto, T. G. Debelee, D. Yohannes, and F. Schwenker, “Automatic pec toral muscle removal in mammograms,” Evolving Systems, pp. 1–8, 2
dc.relationA. Rampun, K. L´opez-Linares, P. J. Morrow, B. W. Scotney, H. Wang, I. G. Oca˜na, G. Maclair, R. Zwiggelaar, M. A. G. Ballester, and I. Mac´ıa, “Breast pec toral muscle segmentation in mammograms using a modified holistically-nested edge detection network,” Medical image analysis, vol. 57, pp. 1–17, 2019
dc.relationM. Alsheh Ali, M. Eriksson, K. Czene, P. Hall, and K. Humphreys, “Detection of potential microcalcification clusters using multivendor for-presentation digital mammograms for short-term breast cancer risk estimation,” Medical physics, vol. 46, no. 4, pp. 1938–1946, 2019.
dc.relationE. D. Avalos-Rivera and A. D. J. Pastrana-Palma, “Classifying microcalcifica tions on digital mammography using morphological descriptors and artificial neu ral network,” in IEEE CACIDI 2016-IEEE Conference on Computer Sciences. IEEE, 2016, pp. 1–4.
dc.relationE. S. McDonald, A. M. McCarthy, S. P. Weinstein, M. D. Schnall, and E. F. Conant, “Bi-rads category 3 comparison: probably benign category after recall from screening before and after implementation of digital breast tomosynthesis,” Radiology, vol. 285, no. 3, pp. 778–787, 2017
dc.relationC. Lei, W. Wei, Z. Liu, Q. Xiong, C. Yang, M. Yang, L. Zhang, T. Zhu, X. Zhuang, C. Liu et al., “Mammography-based radiomic analysis for predicting benign bi rads category 4 calcifications,” European journal of radiology, vol. 121, p. 108711, 2019.
dc.relationS. Aminololama-Shakeri, C. K. Abbey, P. Gazi, N. D. Prionas, A. Nosratieh, C.-S. Li, J. M. Boone, and K. K. Lindfors, “Differentiation of ductal carcinoma in-situfrom benign micro-calcifications by dedicated breast computed tomography,” European journal of radiology, vol. 85, no. 1, pp. 297–303, 2016.
dc.relationV. Shinde and B. T. Rao, “Novel approach to segment the pectoral muscle in the mammograms,” in Cognitive Informatics and Soft Computing. Springer, 2019, pp. 227–237.
dc.relationR. Shen, K. Yan, F. Xiao, J. Chang, C. Jiang, and K. Zhou, “Automatic pec toral muscle region segmentation in mammograms using genetic algorithm and morphological selection,” Journal of digital imaging, vol. 31, no. 5, pp. 680–691, 2018.
dc.relationG. Toz and P. Erdogmus, “A single sided edge marking method for detecting pectoral muscle in digital mammograms,” Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2367–2373, 2018
dc.relationM. S. Salama, A. S. Eltrass, and H. M. Elkamchouchi, “An improved approach for computer-aided diagnosis of breast cancer in digital mammography,” in 2018 IEEE international symposium on medical measurements and applications (Me MeA). IEEE, 2018, pp. 1–5.
dc.relationI. I. Eserner, S. Ergin, and T. Y¨uksel, “A novel multistage system for the de tection and removal of pectoral muscles in mammograms,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 26, no. 1, pp. 35–49, 2018.
dc.relationS. A. Taghanaki, Y. Liu, B. Miles, and G. Hamarneh, “Geometry-based pecto ral muscle segmentation from mlo mammogram views,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 11, pp. 2662–2671, 2017.
dc.relationJ. Wang and Y. Yang, “A context-sensitive deep learning approach for micro calcification detection in mammograms,” Pattern recognition, vol. 78, pp. 12–22, 2018.
dc.relationE. A. Rashed et al., “Neural networks approach for mammography diagnosis using wavelets features,” arXiv preprint arXiv:2003.03000, 2020.
dc.relationN. L. K. Sari, P. Prajitno, L. E. Lubis, and D. S. Soejoko, “Computer aided diagnosis (cad) for mammography with markov random field method with simu lated annealing optimization,” Journal of Medical Physics and Biophysics, vol. 4, no. 1, pp. 85–94, 2017.
dc.relationS. Sasikala, M. Ezhilarasi et al., “Fusion of k-gabor features from medio-lateral oblique and craniocaudal view mammograms for improved breast cancer diagno sis,” Journal of cancer research and therapeutics, vol. 14, no. 5, p. 1036, 2
dc.relationC. Ancy and L. S. Nair, “Tumour classification in graph-cut segmented mam mograms using glcm features-fed svm,” in Intelligent Engineering Informatics. Springer, 2018, pp. 197–208.
dc.relationL. Tsochatzidis, K. Zagoris, M. Savelonas, N. Papamarkos, I. Pratikakis, N. Ariki dis, and L. Costaridou, “Microcalcification oriented content-based mammogram retrieval for breast cancer diagnosis,” in 2014 IEEE International Conference on Imaging Systems and Techniques (IST) Proceedings. IEEE, 2014, pp. 257–262.
dc.relationJ. Wang, Y. Yang, M. N. Wernick, and R. M. Nishikawa, “An image-retrieval aided diagnosis system for clustered microcalcifications,” in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE, 2016, pp. 1076– 1079.
dc.relationR. Nakayama, H. Abe, J. Shiraishi, and K. Doi, “Potential usefulness of similar images in the differential diagnosis of clustered microcalcifications on mammo grams,” Radiology, vol. 253, no. 3, pp. 625–631, 2009.
dc.relationL. Wei, Y. Yang, and R. M. Nishikawa, “Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis,” Pattern recogni tion, vol. 42, no. 6, pp. 1126–1132, 2009
dc.relationH. Jing, Y. Yang, and R. M. Nishikawa, “Retrieval boosted computer-aided diag nosis of clustered microcalcifications for breast cancer,” Medical Physics, vol. 39, no. 2, pp. 676–685, 2012
dc.relationW. Jian, X. Sun, and S. Luo, “Computer-aided diagnosis of breast microcalcifi cations based on dual-tree complex wavelet transform,” Biomedical engineering online, vol. 11, no. 1, pp. 1–12, 2012
dc.relationJ. Nagi, S. A. Kareem, F. Nagi, and S. K. Ahmed, “Automated breast profile segmentation for roi detection using digital mammograms,” in 2010 IEEE EMBS conference on biomedical engineering and sciences (IECBES). IEEE, 2010, pp. 87–92.
dc.relationM. Slavkovi´c-Ili´c, A. Gavrovska, M. Milivojevi´c, I. Reljin, and B. Reljin, “Breast region segmentation and pectoral muscle removal in mammograms,” Telfor Jour nal, vol. 8, no. 1, pp. 50–55, 20
dc.relationC. Kanan and G. W. Cottrell, “Color-to-grayscale: does the method matter in image recognition?” PloS one, vol. 7, no. 1, p. e29740, 2012.
dc.relationS. A. Agnes, J. Anitha, S. I. A. Pandian, and J. D. Peter, “Classification of mam mogram images using multiscale all convolutional neural network (ma-cnn),” Journal of medical systems, vol. 44, no. 1, pp. 1–9, 2020.
dc.relationK. A. H. G´omez, J. D. Echeverry-Correa, and A. ´ A. O. Guti´errez, “Automatic ´ pectoral muscle removal and microcalcification localization in digital mammo grams,” Healthcare Informatics Research, vol. 27, no. 3, p. 222, 2021
dc.relationN. Shrivastava and J. Bharti, “Breast tumor detection in digital mammogram based on efficient seed region growing segmentation,” IETE Journal of Research, pp. 1–13, 2020
dc.relationN. Alam and M. J. Islam, “Pectoral muscle elimination on mammogram using k means clustering approach,” International Journal of Computer Vision & Signal Processing, vol. 4, no. 1, pp. 11–21, 2014.
dc.relationM. Mustra and M. Grgic, “Robust automatic breast and pectoral muscle seg mentation from scanned mammograms,” Signal processing, vol. 93, no. 10, pp. 2817–2827, 2013.
dc.relationJ. L. Rodr´ıguez Cardona et al., “Identificaci´on de microcalcificaciones en mamo graf´ıas usando redes neuronales convolucionales,” B.S. thesis, Uniandes.
dc.relationJ. J. Van Griethuysen, A. Fedorov, C. Parmar, A. Hosny, N. Aucoin, V. Narayan, R. G. Beets-Tan, J.-C. Fillion-Robin, S. Pieper, and H. J. Aerts, “Computatio nal radiomics system to decode the radiographic phenotype,” Cancer research, vol. 77, no. 21, pp. e104–e107, 2017.
dc.relationS. Haykin, Neural networks and learning machines, 3/E. Pearson Education India, 2010.
dc.relationY. Anzai, Pattern recognition and machine learning. Elsevier, 2012.
dc.relationP. SUCKLING J, “The mammographic image analysis society digital mammo gram database,” Digital Mammo, pp. 375–386, 19
dc.relation] C. Rose, D. Turi, A. Williams, K. Wolstencroft, and C. Taylor, “Web services for the ddsm and digital mammography research,” in International workshop on digital mammography. Springer, 2006, pp. 376–383.
dc.relationA. Qayyum and A. Basit, “Automatic breast segmentation and cancer detec tion via svm in mammograms,” in 2016 International conference on emerging technologies (ICET). IEEE, 2016, pp. 1–6.
dc.relationW. B. Yoon, J. E. Oh, E. Y. Chae, H. H. Kim, S. Y. Lee, and K. G. Kim, “Automatic detection of pectoral muscle region for computer-aided diagnosis using mias mammograms,” BioMed research international, vol. 2016, 2016.
dc.relationK. Wei, W. Guangzhi, and D. Hui, “Segmentation of the breast region in mammo grams using watershed transformation,” in 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2006, pp. 6500–6503.
dc.relationS. Sreedevi and E. Sherly, “A novel approach for removal of pectoral muscles in digital mammogram,” Procedia Computer Science, vol. 46, pp. 1724–1731, 2015.
dc.relationD. Raba, A. Oliver, J. Mart´ı, M. Peracaula, and J. Espunya, “Breast segmen tation with pectoral muscle suppression on digital mammograms,” in Iberian conference on pattern recognition and image analysis. Springer, 2005, pp. 471– 478.
dc.relationN. Saltanat, M. A. Hossain, and M. S. Alam, “An efficient pixel value based mapping scheme to delineate pectoral muscle from mammograms,” in 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Appli cations (BIC-TA). IEEE, 2010, pp. 1510–151
dc.relationL. Wang, M.-l. Zhu, L.-p. Deng, and X. Yuan, “Automatic pectoral muscle boun dary detection in mammograms based on markov chain and active contour mo del,” Journal of Zhejiang University SCIENCE C, vol. 11, no. 2, pp. 111–118, 2010
dc.relationY. Li, H. Chen, Y. Yang, and N. Yang, “Pectoral muscle segmentation in mam mograms based on homogenous texture and intensity deviation,” Pattern Recog nition, vol. 46, no. 3, pp. 681–691, 2013.
dc.relationB. Graham, “Fractional max-pooling,” arXiv preprint arXiv:1412.6071, 2014.
dc.relationA. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, vol. 25, pp. 1097–1105, 2012.
dc.relationC. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Procee dings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9.
dc.relationJ. Wang, R. M. Nishikawa, and Y. Yang, “Global detection approach for clustered microcalcifications in mammograms using a deep learning network,” Journal of Medical Imaging, vol. 4, no. 2, p. 024501, 2017.
dc.relationX. Liu, M. Mei, J. Liu, and W. Hu, “Microcalcification detection in full-field digital mammograms with pfcm clustering and weighted svm-based method,” EURASIP Journal on Advances in Signal Processing, vol. 2015, no. 1, pp. 1–13, 2015.
dc.relationI. El-Naqa, Y. Yang, M. N. Wernick, N. P. Galatsanos, and R. M. Nishikawa, “A support vector machine approach for detection of microcalcifications,” IEEE transactions on medical imaging, vol. 21, no. 12, pp. 1552–1563, 2002.
dc.relationR. Gallardo-Caballero, C. Garc´ıa-Orellana, A. Garc´ıa-Manso, H. Gonz´alez Velasco, and M. Mac´ıas-Mac´ıas, “Independent component analysis to detect clus tered microcalcification breast cancers,” The Scientific World Journal, vol. 2012, 2012.
dc.rightsManifiesto (Manifestamos) en este documento la voluntad de autorizar a la Biblioteca Jorge Roa Martínez de la Universidad Tecnológica de Pereira la publicación en el Repositorio institucional (http://biblioteca.utp.edu.co), la versión electrónica de la OBRA titulada: ________________________________________________________________________________________________ ________________________________________________________________________________________________ ________________________________________________________________________________________________ La Universidad Tecnológica de Pereira, entidad académica sin ánimo de lucro, queda por lo tanto facultada para ejercer plenamente la autorización anteriormente descrita en su actividad ordinaria de investigación, docencia y publicación. La autorización otorgada se ajusta a lo que establece la Ley 23 de 1982. Con todo, en mi (nuestra) condición de autor (es) me (nos) reservo (reservamos) los derechos morales de la OBRA antes citada con arreglo al artículo 30 de
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dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
dc.subjectPattern classification
dc.subjectBio-inspired computing
dc.subjectDeep learning
dc.subjectMicrocalcificaciones
dc.subjectBI-RADS
dc.subjectAprendizaje automático
dc.subjectClasificación
dc.titlePrototipo de un sistema de diagnóstico asistido por ordenador orientado a la localización de clusters de microcalcificaciones en mamografías
dc.typeTrabajo de grado - Maestría
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dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa
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