Quantum measurement learning for medical image classification
ilustraciones, graficas, tablas
- Autores:
-
Diego Hernando, Useche Reyes
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81541
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::003 - Sistemas
CANCER-FORMACION DE IMAGENES
Cancer-imaging
Quantum measurement classification
Prostate cancer
Deep learning
Quantum machine learning
clasificación con medición cuántica
cáncer de próstata
aprendizaje profundo
aprendizaje automático cuántico
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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Quantum measurement learning for medical image classification |
| dc.title.translated.spa.fl_str_mv |
Aprendizaje con medición cuántica para la clasificación de imágenes médicas |
| title |
Quantum measurement learning for medical image classification |
| spellingShingle |
Quantum measurement learning for medical image classification 000 - Ciencias de la computación, información y obras generales::003 - Sistemas CANCER-FORMACION DE IMAGENES Cancer-imaging Quantum measurement classification Prostate cancer Deep learning Quantum machine learning clasificación con medición cuántica cáncer de próstata aprendizaje profundo aprendizaje automático cuántico |
| title_short |
Quantum measurement learning for medical image classification |
| title_full |
Quantum measurement learning for medical image classification |
| title_fullStr |
Quantum measurement learning for medical image classification |
| title_full_unstemmed |
Quantum measurement learning for medical image classification |
| title_sort |
Quantum measurement learning for medical image classification |
| dc.creator.fl_str_mv |
Diego Hernando, Useche Reyes |
| dc.contributor.advisor.none.fl_str_mv |
Fabio Augusto, Gonzaléz Osorio |
| dc.contributor.author.none.fl_str_mv |
Diego Hernando, Useche Reyes |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas |
| topic |
000 - Ciencias de la computación, información y obras generales::003 - Sistemas CANCER-FORMACION DE IMAGENES Cancer-imaging Quantum measurement classification Prostate cancer Deep learning Quantum machine learning clasificación con medición cuántica cáncer de próstata aprendizaje profundo aprendizaje automático cuántico |
| dc.subject.lemb.spa.fl_str_mv |
CANCER-FORMACION DE IMAGENES |
| dc.subject.lemb.eng.fl_str_mv |
Cancer-imaging |
| dc.subject.proposal.eng.fl_str_mv |
Quantum measurement classification Prostate cancer Deep learning Quantum machine learning |
| dc.subject.proposal.spa.fl_str_mv |
clasificación con medición cuántica cáncer de próstata aprendizaje profundo aprendizaje automático cuántico |
| description |
ilustraciones, graficas, tablas |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-06-08T20:51:34Z |
| dc.date.available.none.fl_str_mv |
2022-06-08T20:51:34Z |
| dc.date.issued.none.fl_str_mv |
2022-03 |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/81541 |
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Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/81541 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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eng |
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eng |
| dc.relation.references.spa.fl_str_mv |
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G. Thompson, J. C. Matthews, T. Hashimoto, J. L. O’Brien, and A. Laing, “Universal linear optics,” Science, vol. 349, no. 6249, pp. 711–716, 8 2015. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/26160375/ A. Sit, F. Bouchard, R. Fickler, J. Gagnon-Bischoff, H. Larocque, K. Heshami, D. Elser, C. Peuntinger, K. Gunthner, B. Heim, C. Marquardt, G. Leuchs, R. W. Boyd, and E. Karimi, “High-dimensional intracity quantum cryptography with structured photons,” Optica, vol. 4, no. 9, p. 1006, 9 2017. [Online]. Available: https://doi.org/10.1364/OPTICA.4.001006 A. B. Klimov, R. Guzm ́an, J. C. Retamal, and C. Saavedra, “Qutrit quantum computer with trapped ions,” Physical Review A - Atomic, Molecular, and Optical Physics, vol. 67, no. 6, p. 7, 6 2003. [Online]. Available: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.67.062313 Z. Gedik, I. A. Silva, B. C ̧ akmak, G. Karpat, E. L. Vidoto, D. O. Soares-Pinto, E. R. DeAzevedo, and F. F. Fanchini, “Computational speed-up with a single qudit,” Scientific Reports, vol. 5, no. 1, p. 14671, 10 2015. [Online]. Available: www.nature.com/scientificreports/ E. Moreno-Pineda, C. Godfrin, F. Balestro, W. Wernsdorfer, and M. Ruben, “Molecular spin qudits for quantum algorithms,” pp. 501–513, 1 2018. [Online]. Available: https://pubs.rsc.org/en/content/articlehtml/2018/cs/c5cs00933bhttps: //pubs.rsc.org/en/content/articlelanding/2018/cs/c5cs00933b D. Cozzolino, B. Da Lio, D. Bacco, and L. K. Oxenløwe, “High-Dimensional Quantum Communication: Benefits, Progress, and Future Challenges,” Advanced Quantum Technologies, vol. 2, no. 12, p. 1900038, 12 2019. [Online]. Available: https://onlinelibrary.wiley.com/doi/full/10.1002/qute.201900038https: //onlinelibrary.wiley.com/doi/abs/10.1002/qute.201900038https://onlinelibrary.wile y.com/doi/10.1002/qute.201900038 L. Sheridan and V. Scarani, “Security proof for quantum key distribution using qudit systems,” Physical Review A - Atomic, Molecular, and Optical Physics, vol. 82, no. 3, p. 030301, 9 2010. [Online]. Available: https: //journals.aps.org/pra/abstract/10.1103/PhysRevA.82.030301 P. Rebentrost, M. Mohseni, and S. Lloyd, “Quantum support vector machine for big data classification,” Physical Review Letters, vol. 113, no. 3, 9 2014. N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum Deep Learning,” Quantum Information and Computation, vol. 16, no. 7-8, pp. 541–587, 12 2014. [Online]. Available: https://arxiv.org/abs/1412.3489v2 . Lu and S. L. Braunstein, “Quantum decision tree classifier,” Quantum Information Processing 2013 13:3, vol. 13, no. 3, pp. 757–770, 11 2013. [Online]. Available: https://link.springer.com/article/10.1007/s11128-013-0687-5 A. A. Ezhov and D. Ventura, “Quantum Neural Networks.” Physica, Heidelberg, 2000, pp. 213–235. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-7908-1856-7 11 I. Cong, S. Choi, and M. D. Lukin, “Quantum convolutional neural networks,” Nature Physics, vol. 15, no. 12, pp. 1273–1278, 12 2019. [Online]. Available: https://www.nature.com/articles/s41567-019-0648-8 P. L. Dallaire-Demers and N. Killoran, “Quantum generative adversarial networks,” Physical Review A, vol. 98, no. 1, p. 012324, 7 2018. [Online]. Available: https://journals.aps.org/pra/abstract/10.1103/PhysRevA.98.012324 F. A. Cárdenas-López, L. Lamata, J. C. Retamal, and E. Solano, “Multiqubit and multilevel quantum reinforcement learning with quantum technologies,” PLoS ONE, vol. 13, no. 7, p. e0200455, 7 2018. [Online]. Available: https://doi.org/10.1371/journal.pone.0200455 D. N. Diep, “Some Quantum Neural Networks,” International Journal of Theoretical Physics, vol. 59, no. 4, pp. 1179–1187, 4 2020. [Online]. Available: https://link.springer.com/article/10.1007/s10773-020-04397-1 B. Ricks and D. Ventura, “Training a Quantum Neural Network,” Advances in Neural Information Processing Systems, vol. 16, 2003. K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, and R. Wolf, “Efficient learning for deep quantum neural networks,” pp. 1–6, 2 2019. [Online]. Available: https://doi.org/10.1038/s41467-020-14454-2 A. Giraldo-Carvajal and J. A. Jaramillo-Villegas, “QuantumSkynet: A High- Dimensional Quantum Computing Simulator,” Optics InfoBase Conference Papers, 6 2021. [Online]. Available: https://arxiv.org/abs/2106.15833v1 F. S. Khan and M. Perkowski, “Synthesis of multi-qudit hybrid and d-valued quantum logic circuits by decomposition,” Theoretical Computer Science, vol. 367, no. 3, pp. 336–346, 12 2006. |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Fabio Augusto, Gonzaléz Osorio89dcb2596792ec8ae0085fa9e2908a87Diego Hernando, Useche Reyesb2eb7c6c4cf73215241c1ad8b37337272022-06-08T20:51:34Z2022-06-08T20:51:34Z2022-03https://repositorio.unal.edu.co/handle/unal/81541Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficas, tablasDeep neural networks are the state-of-the-art for medical image classification. However, these models require large data sets to be trained, and they lack some interpretability on their predictions. In recent years, there has been a growing interests of using the statistical machinery of quantum mechanics to built novel machine learning models, which may run on classical or quantum computers. One of such models is the recently proposed method quantum measurement classification (QMC) [1]. In this thesis, we present various classical-quantum machine learning strategies that combine convolutional neural networks (CNNs) with methods based on QMC [2] to the task of learning medical images in a supervised manner. We first approach the problem with a deep probabilistic regression model, showing that is competitive, and more interpretable compared to conventional deep learning architectures. We then present a representation learning technique based on CNNs which maps medical images to pure and mixed quantum states, and show that its competitive with other representation learning strategies. In addition, we propose a quantum implementation of two QMC-based models on a high-dimensional quantum computer, we demonstrate that it is possible to perform classification and density estimation in a quantum computer.Las redes neuronales profundas están a la vanguardia para la clasificación de imágenes médicas. Sin embargo, estos modelos requieren para su entrenamiento conjuntos de datos muy grandes, y a sus predicciones les falta interpretabilidad. Recientemente, se han propuesto varios métodos de inteligencia artificial basados en la mecánica cuántica, los cuales pueden ser implementados en computadores clásicos o cuánticos. Uno de estos métodos es el recientemente propuesto \textit{Quantum Measurement Classification} (QMC) [1]. En este trabajo de tesis, presentamos diferentes estrategias clásicas y cuánticas de aprendizaje automático, las cuales combinan las redes neuronales convolucionales (CNNs) y algunos métodos basados en QMC [2] para la tarea de aprendizaje supervisado de imagenes medicas. En primer lugar, planteamos el problema de clasificación con un modelo de regresión profundo y probabilístico, mostrando que es competitivo y más interpretable en comparación a arquitecturas convencionales de aprendizaje profundo. En segundo lugar, presentamos un método de aprendizaje de la representación basado en CNNs del cual se obtienen características de las imágenes médicas en forma de estados cuánticos puros y mezclados, y mostramos que los resultados del método son competitivos con otras estrategias de representación. Adicionalmente, proponemos una implementación cuántica de dos métodos de aprendizaje automático basados en QMC en un computador cuántico de altas dimensiones, mostrando que es posible el aprendizaje supervisado y la estimación de la densidad en un computador cuántico. (Texto tomado de la fuente)MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y Computación59 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::003 - SistemasCANCER-FORMACION DE IMAGENESCancer-imagingQuantum measurement classificationProstate cancerDeep learningQuantum machine learningclasificación con medición cuánticacáncer de próstataaprendizaje profundoaprendizaje automático cuánticoQuantum measurement learning for medical image classificationAprendizaje con medición cuántica para la clasificación de imágenes médicasTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMF. A. González, V. 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Perkowski, “Synthesis of multi-qudit hybrid and d-valued quantum logic circuits by decomposition,” Theoretical Computer Science, vol. 367, no. 3, pp. 336–346, 12 2006.EstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81541/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54ORIGINAL1026569527.2022.pdf1026569527.2022.pdfTesis de Maestría en Sistemas y Computaciónapplication/pdf1115684https://repositorio.unal.edu.co/bitstream/unal/81541/3/1026569527.2022.pdf77f6f34a8c9715ddd655748d13fc6727MD53THUMBNAIL1026569527.2022.pdf.jpg1026569527.2022.pdf.jpgGenerated Thumbnailimage/jpeg4479https://repositorio.unal.edu.co/bitstream/unal/81541/5/1026569527.2022.pdf.jpg5cbb3a77b1bab9c49cec28d5bdf0ddb0MD55unal/81541oai:repositorio.unal.edu.co:unal/815412023-08-05 23:03:35.937Repositorio Institucional Universidad Nacional de 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