Abstract
Deep Learning (DL) is a new area of Machine Learning research introduced with the objective of moving Machine Learning closer to one of its original goals, i.e., Artificial Intelligence (AI). DL breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. Better preventive healthcare, even better recommendations, are all here today or on the horizon. However, keeping up the pace of progress will require confronting currently AI’s serious limitations. The last but not the least, Cervical Carcinoma is actuality a critical public health problem. Although patients have a longer survival rate due to early diagnosis and more effective treatment, this disease is still the leading cause of cancer death among women. Therefore, the main objective of this article is to present a DL approach to Case Based Reasoning in order to evaluate and diagnose Cervical Carcinoma using Magnetic Resonance Imaging. It will be grounded on a dynamic virtual world of complex and interactive entities that compete against one another in which its aptitude is judged by a single criterion, the Quality of Information they carry and the system’s Degree of Confidence on such a measure, under a fixed symbolic structure.
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References
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, pp. 318–362. MIT Press, Cambridge (1986)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Sommers, J.: Is AI Riding a One-Trick Pony? https://www.technologyreview.com/s/608911/is-ai-riding-a-one-trick-pony/
Sousa, A.: Cervical Cancer: Trends and Studies. Fernando Pessoa University Edition, Oporto (2011)
Deng, S., Zhu, L., Huang, D.: Predicting hub genes associated with cervical cancer through gene co-expression networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 13, 27–35 (2016)
U.S. Cancer Statistics Working Group: United States Cancer Statistics: 1999–2014 Incidence and Mortality Web-based Report. http://www.cdc.gov/uscs
American Cancer Society: What is cervical cancer? https://www.cancer.org/cancer/cervical-cancer/about/what-is-cervical-cancer.html
Remya, V., Lekshmi-Priya V.L.: Simultaneous segmentation and tumor detection in MRI cervical cancer radiation therapy with hierarchical adaptive local affine registration. In: Proceedings of the 2014 International Conference on Computer Communication and Informatics, 6 pp. IEEE Edition (2014)
Leake, D.B.: Case-Based Reasoning: Experiences, Lessons and Future Directions. MIT Press Cambridge, Massachusetts (1996)
Mithlesh, A., Namita, M., Girdhari, S.: Cervical cancer detection using segmentation on Pap smear images. In: Proceedings of the 1st International Conference on Informatics and Analytics, Article 29, Association for Computing Machinery, New York (2016)
Vaccarella, S., Lortet-Tieulent, J., Plummer, M., Franceschi, S., Bray, F.: Worldwide trends in cervical cancer incidence: Impact of screening against changes in disease risk factors. Eur. J. Cancer 49, 3262–3273 (2013)
Pecorelli, S.: Corrigendum to “Revised FIGO staging for carcinoma of the vulva, cervix, and endometrium”. Int. J. Gynecol. Obstet. 108, 176 (2010)
Kakas, A., Kowalski, R., Toni, F.: The role of abduction in logic programming. In: Gabbay, D., Hogger, C., Robinson, I. (eds.) Handbook of Logic in Artificial Intelligence and Logic Programming, vol. 5, pp. 235–324. Oxford University Press, Oxford (1998)
Pereira, L., Anh, H.: Evolution prospection. In: Nakamatsu, K. (ed.) Studies in Computational Intelligence, vol. 199, pp. 51–64. Springer, Berlin (2009)
Neves, J., Machado, J., Analide, C., Abelha, A., Brito, L.: The halt condition in genetic programming. In: Neves, J., Santos, M.F., Machado, J. (eds.) Progress in Artificial Intelligence. LNAI, vol. 4874, pp. 160–169. Springer, Berlin (2007)
Neves, J.: A logic interpreter to handle time and negation in logic databases. In: Muller, R., Pottmyer, J. (eds.) Proceedings of the 1984 annual conference of the ACM on the 5th Generation Challenge, pp. 50–54. Association for Computing Machinery, New York (1984)
Machado J., Abelha A., Novais P., Neves J., Neves J.: Quality of service in healthcare units. In Bertelle, C., Ayesh, A. (eds.) Proceedings of the ESM 2008, pp. 291–298. Eurosis – ETI Publication, Ghent (2008)
Fernandes, A., Vicente, H., Figueiredo, M., Neves, M., Neves, J.: An adaptive and evolutionary model to assess the organizational efficiency in training corporations. In: Dang, T.K., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds.) Future Data and Security Engineering. Lecture Notes on Computer Science, vol. 10018, pp. 415–428. Springer International Publishing, Cham (2016)
Fernandes, F., Vicente, H., Abelha, A., Machado, J., Novais, P., Neves J.: Artificial Neural Networks in Diabetes Control. In Proceedings of the 2015 Science and Information Conference (SAI 2015), pp. 362–370. IEEE Edition (2015)
Quintas, A., Vicente, H., Novais, P., Abelha, A., Santos, M.F., Machado, J., Neves, J.: A case based approach to assess waiting time prediction at an intensive care unity. In: Arezes, P. (ed.) Advances in Safety Management and Human Factors. Advances in Intelligent Systems and Computing, vol. 491, pp. 29–39. Springer International Publishing, Cham (2016)
Silva, A., Vicente, H., Abelha, A., Santos, M.F., Machado, J., Neves, J., Neves, J.: Length of stay in intensive care units—a case base evaluation. In: Fujita, H., Papadopoulos, G.A. (eds.) New Trends in Software Methodologies, Tools and Techniques, Frontiers in Artificial Intelligence and Applications, vol. 286, pp. 191–202. IOS Press, Amsterdam (2016)
3D Slicer. https://www.slicer.org/wiki/Documentation/Nightly/Modules/SimpleFilters
Perdigão, N., Tavares, J.M., Martins, J.A., Pires, E.B., Jorge, R. M.: Sobre a Geração de Malhas Tridimensionais para fins computacionais a partir de imagens médicas. In Aparicio, J.L., Ferran, A.R., Martins, J.A., Gallego, R., Sá, J.C. (eds.) Proceedings of the Congreso de Métodos Numéricos en Ingeniería, 16 pp. SEMNI, Barcelona (2005)
Hasan, D.I., Enaba, M.M., El-Rahman, H.M.A., El-Shazely, S.: Apparent diffusion coefficient value in evaluating types, stages and histologic grading of cancer cervix. Egypt. J. Radiol. Nuclear Med. 46, 781–789 (2015)
Camisão, C., Brenna, C., Lombardelli, S., Djahjah, K., Zeferino, L.: Magnetic resonance imaging in the staging of cervical cancer. Radiologia Brasileira 40, 207–215 (2007)
The Cancer Imaging Archive (TCIA). http://www.cancerimagingarchive.net/
Acknowledgements
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT—Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.
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Neves, J. et al. (2018). A Deep Learning Approach to Case Based Reasoning to the Evaluation and Diagnosis of Cervical Carcinoma. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_16
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