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HMDHBN: Hidden Markov Inducing a Dynamic Hierarchical Bayesian Network for Tumor Growth Prediction

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Computer Analysis of Images and Patterns (CAIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11678))

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Abstract

Radiomics transform medical images into a rich source of information and a main tool for the tumor growth survey, which is the result of multiple processes at different scales composing a complex system. To model the tumor evolution in both time and space we propose to exploit radiomic features within a multi-scale architecture that models the biological events at different levels. The proposed framework is based on the HMM architecture that encodes the relation between radiomic features as observed phenomena and the mechanical interactions within the tumor as a hidden process. On the other hand, it models the Tumor evolution through time thanks to its dynamic aspect. While, to represent the biological interactions, we use a Hierarchical Bayesian Network where we associate a level for each scale (Tissue, cell-cluster, cell scale). Thus, the HMM induces a Dynamic Hierarchical Bayesian Network that encodes the tumor growth aspects and factors.

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References

  1. Jemal, A., et al.: Annual report to the nation on the status of cancer, 1975–2014, featuring survival. JNCI: J. Natl. Cancer Inst. 109(9), djx030 (2017). Clerk Maxwell, J.: A Treatise on Electricity and Magnetism, 3rd edn, vol. 2, pp. 68–73. Clarendon, Oxford (1892)

    Google Scholar 

  2. Malvezzi, M., et al.: European cancer mortality predictions for the year 2018 with focus on colorectal cancer. Ann. Oncol. 29(4), 1016–1022 (2018)

    Article  Google Scholar 

  3. Hornberg, J.J., Bruggeman, F.J., Westerhoff, H.V., Lankelma, J.: Cancer: a systems biology disease. Biosystems 83(2–3), 81–90 (2006). https://doi.org/10.1016/j.biosystems.2005.05.014

    Article  Google Scholar 

  4. Masoudi-Nejad, A., Wang, E.: Cancer modeling and network biology: accelerating toward personalized medicine. In: Seminars in Cancer Biology, vol. 30, pp. 1–3. Academic Press, February 2015

    Article  Google Scholar 

  5. Feng, Y., Boukhris, S.J., Ranjan, R., Valencia, R.A.: Biological systems: multiscale modeling based on mixture theory. In: De, S., Hwang, W., Kuhl, E. (eds.) Multiscale Modeling in Biomechanics and Mechanobiology, pp. 257–286. Springer, London (2015). https://doi.org/10.1007/978-1-4471-6599-6_11

    Chapter  Google Scholar 

  6. Masoudi-Nejad, A., Bidkhori, G., Ashtiani, S.H., Najafi, A., Bozorgmehr, J.H., Wang, E.: Cancer systems biology and modeling: microscopic scale and multiscale approaches. In: Seminars in Cancer Biology, vol. 30, pp. 60–69. Academic Press, February 2015

    Article  Google Scholar 

  7. Ghadiri, M., Heidari, M., Marashi, S.A., Mousavi, S.H.: A multiscale agent-based framework integrated with a constraint-based metabolic network model of cancer for simulating avascular tumor growth. Mol. BioSyst. 13(9), 1888–1897 (2017)

    Article  Google Scholar 

  8. Zhang, L., Lu, L., Summers, R.M., Kebebew, E., Yao, J.: Personalized pancreatic tumor growth prediction via group learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 424–432. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66185-8_48

    Chapter  Google Scholar 

  9. Zhang, L., Lu, L., Summers, R.M., Kebebew, E., Yao, J.: Convolutional invasion and expansion networks for tumor growth prediction. IEEE Trans. Med. Imaging 37(2), 638–648 (2018)

    Article  Google Scholar 

  10. Weizman, L., et al.: Prediction of brain MR scans in longitudinal tumor follow-up studies. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 179–187. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33418-4_23

    Chapter  Google Scholar 

  11. Ibargüengoytia, P.H., Reyes, A., García, U.A., Romero, I., Pech, D.: Evaluating probabilistic graphical models for forecasting. In: 2015 18th International Conference on Intelligent System Application to Power Systems (ISAP), pp. 1–6. IEEE, September 2015

    Google Scholar 

  12. Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521(7553), 452 (2015)

    Article  Google Scholar 

  13. Lucas, P.: Bayesian networks in medicine: a model-based approach to medical decision making (2001)

    Google Scholar 

  14. Sucar, L.E., Bielza, C., Morales, E.F., Hernandez-Leal, P., Zaragoza, J.H., Larrañaga, P.: Multi-label classification with Bayesian network-based chain classifiers. Pattern Recogn. Lett. 41, 14–22 (2014)

    Article  Google Scholar 

  15. Tauber, S., Navarro, D.J., Perfors, A., Steyvers, M.: Bayesian models of cognition revisited: setting optimality aside and letting data drive psychological theory. Psychol. Rev. 124(4), 410 (2017)

    Article  Google Scholar 

  16. Pearl, J.: Bayesian networks (2011)

    Google Scholar 

  17. Forney, G.D.: The viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)

    Article  MathSciNet  Google Scholar 

  18. Wu, M., Yang, X., Chan, C.: A dynamic analysis of IRS-PKR signaling in liver cells: a discrete modeling approach. PLoS One 4(12), e8040 (2009)

    Article  Google Scholar 

  19. Amiri, S., Rekik, I., Mahjoub, M.A.: Deep random forest-based learning transfer to SVM for brain tumor segmentation. In: 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp. 297–302. IEEE. March 2016

    Google Scholar 

  20. Amiri, S., Rekik, I., Mahjoub, M.A.: Bayesian network and structured random forest cooperative deep learning for automatic multi-label brain tumor segmentation. In: 2018 10th International Conference The International Conference on Agents and Artificial Intelligence (ICAART) (2018)

    Google Scholar 

  21. Amiri, S., Rekik, I., Mahjoub, M.A.: Dynamic multiscale tree learning using ensemble strong classifiers for multi-label segmentation of medical images with lesions. In: 2018 13th International Conference on Computer Vision Theory and Applications (VISAAP) (2018)

    Google Scholar 

  22. Amiri, S., Mahjoub, M.A., Rekik, I.: Dynamic multiscale tree learning using ensemble strong classifiers for multi-label segmentation of medical images with lesions. Neurocomputing (2018)

    Google Scholar 

  23. Gyftodimos, E., Flach, P.A.: Hierarchical bayesian networks: an approach to classification and learning for structured data. In: Vouros, G.A., Panayiotopoulos, T. (eds.) SETN 2004. LNCS (LNAI), vol. 3025, pp. 291–300. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24674-9_31

    Chapter  MATH  Google Scholar 

  24. Rahman, M.M., Feng, Y., Yankeelov, T.E., Oden, J.T.: A fully coupled space-time multiscale modeling framework for predicting tumor growth. Comput. Methods Appl. Mech. Eng. 320, 261–286 (2017)

    Article  MathSciNet  Google Scholar 

  25. Zhu, S., Wang, Y.: Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks. Sci. Rep. 5, 17841 (2015)

    Article  Google Scholar 

  26. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Elsevier (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institut Supérieur d’Informatique et des Techniques de Communication de Hammam Sousse, LATIS - Laboratory of Advanced Technology and Intelligent Systems, University of Sousse, 4011, Sousse, Tunisia

    Samya Amiri

  2. Ecole Nationale d’Ingénieurs de Sousse, LATIS - Laboratory of Advanced Technology and Intelligent Systems, University of Sousse, 4023, Sousse, Tunisia

    Mohamed Ali Mahjoub

Authors
  1. Samya Amiri
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  2. Mohamed Ali Mahjoub
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Corresponding author

Correspondence to Mohamed Ali Mahjoub .

Editor information

Editors and Affiliations

  1. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Mario Vento

  2. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Gennaro Percannella

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Amiri, S., Mahjoub, M.A. (2019). HMDHBN: Hidden Markov Inducing a Dynamic Hierarchical Bayesian Network for Tumor Growth Prediction. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-29888-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29887-6

  • Online ISBN: 978-3-030-29888-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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