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IF3: An Interpretable Feature Fusion Framework for Lesion Risk Assessment Based on Auto-constructed Fuzzy Cognitive Maps

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Cancer Prevention Through Early Detection (CaPTion 2022)

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Abstract

The detection of abnormalities in the gastrointestinal (GI) tract, including precancerous lesions, is substantially subject to expert knowledge and experience. To address the challenge of automated lesion risk assessment, based on Wireless Capsule Endoscopy (WCE) images, this paper introduces a novel Artificial Intelligence (AI) framework based on Fuzzy Cognitive Maps (FCMs). Specifically, FCMs are fuzzy graph structures used to model knowledge spaces using cause-and-effect relationships, enabling uncertainty-aware reasoning and inference. The novel proposed Interpretable FCM-based Feature Fusion (IF3) framework, includes the following contributions: a) it automatically constructs an FCM based on similarities discovered in training data; b) it enables the fusion of different features extracted using different methods. The proposed framework is generic, domain-independent and it can be integrated into any classifier. To demonstrate its performance, experiments were conducted using real datasets, which include a variety of GI abnormalities, and different feature extractors. The results show that the automatically constructed FCM outperforms state-of-the-art methods, while providing interpretable results, in an easily understandable way.

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References

  1. Wang, S., Shen, L., Luo, H.: Application of linked color imaging in the diagnosis of early gastrointestinal neoplasms and precancerous lesions: a review. Ther. Adv. Gastroenterol. 14, 17562848211025924 (2021)

    Article  Google Scholar 

  2. Kim, D.H.: Other small bowel tumors. In: Chun, H.J., Seol, S.-Y., Choi, M.-G., Cho, J.Y. (eds.) Small Intestine Disease, pp. 243–248. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7239-2_47

    Chapter  Google Scholar 

  3. Dray, X., et al.: Artificial intelligence in small bowel capsule endoscopy-current status, challenges and future promise. J. Gastroenterol. Hepatol. 36(1), 12–19 (2021)

    Article  Google Scholar 

  4. Vasilakakis, M., Koulaouzidis, A., Yung, D.E., Plevris, J.N., Toth, E., Iakovidis, D.K.: Follow-up on: optimizing lesion detection in small bowel capsule endoscopy and beyond: from present problems to future solutions. Expert Rev. Gastroenterol. Hepatol. 13(2), 129–141 (2019)

    Article  Google Scholar 

  5. Painuli, D., Bhardwaj, S., et al.: Recent advancement in cancer diagnosis using machine learning and deep learning techniques: a comprehensive review. Comput. Biol. Med. 105580 (2022)

    Google Scholar 

  6. Diamantis, D.E., Iakovidis, D.K., Koulaouzidis, A.: Look-behind fully convolutional neural network for computer-aided endoscopy. Biomed. Signal Process. Control 49, 192–201 (2019)

    Article  Google Scholar 

  7. Iakovidis, D.K., Georgakopoulos, S.V., Vasilakakis, M., Koulaouzidis, A., Plagianakos, V.P.: Detecting and locating gastrointestinal anomalies using deep learning and iterative cluster unification. IEEE Trans. Med. Imaging 37(10), 2196–2210 (2018)

    Article  Google Scholar 

  8. Yuan, Y., Li, B., Meng, M.Q.-H.: Improved bag of feature for automatic polyp detection in wireless capsule endoscopy images. IEEE Trans. Autom. Sci. Eng. 13(2), 529–535 (2015)

    Article  Google Scholar 

  9. Angelov, P.P., Soares, E.A., Jiang, R., Arnold, N.I., Atkinson, P.M.: Explainable artificial intelligence: an analytical review. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 11(5), e1424 (2021)

    Google Scholar 

  10. Prosperi, M., et al.: Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nature Mach. Intell. 2(7), 369–375 (2020)

    Article  Google Scholar 

  11. Vasilakakis, M., Sovatzidi, G., Iakovidis, D.K.: Explainable classification of weakly annotated wireless capsule endoscopy images based on a fuzzy bag-of-colour features model and brain storm optimization. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 488–498 (2021)

    Google Scholar 

  12. Tiddi, I., Schlobach, S.: Knowledge graphs as tools for explainable machine learning: a survey. Artif. Intell. 302, 103627 (2022)

    Article  MathSciNet  Google Scholar 

  13. Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24(1), 65–75 (1986)

    Article  Google Scholar 

  14. Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K., Bello, R.: A review on methods and software for fuzzy cognitive maps. Artif. Intell. Rev. 52(3), 1707–1737 (2017). https://doi.org/10.1007/s10462-017-9575-1

    Article  Google Scholar 

  15. Mizumoto, M., Tanaka, K.: Fuzzy sets and their operations. Inf. Control 48(1), 30–48 (1981)

    Article  MathSciNet  Google Scholar 

  16. Vasilakakis, M.D., Iakovidis, D.K., Spyrou, E., Koulaouzidis, A.: DINOSARC: color features based on selective aggregation of chromatic image components for wireless capsule endoscopy. Comput. Math. Meth. Med. 2018 (2018)

    Google Scholar 

  17. Koulaouzidis, A., et al.: KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes. Endosc. Int. Open 5(06), E477–E483 (2017)

    Google Scholar 

  18. Smedsrud, P.H., et al.: Kvasir-Capsule, a video capsule endoscopy dataset. Sci. Data 8(1), 1–10 (2021)

    Google Scholar 

  19. Drake, J., Hamerly, G.: Accelerated k-means with adaptive distance bounds. In: 5th NIPS Workshop on Optimization for Machine Learning, vol. 8 (2012)

    Google Scholar 

  20. Vasilakakis, M., Iakovidis, D.K., Spyrou, E., Koulaouzidis, A.: Weakly-supervised lesion detection in video capsule endoscopy based on a bag-of-colour features model. In: International Workshop on Computer-Assisted and Robotic Endoscopy, pp. 96–103 (2016)

    Google Scholar 

  21. Nápoles, G., Espinosa, M.L., Grau, I., Vanhoof, K.: FCM expert: software tool for scenario analysis and pattern classification based on fuzzy cognitive maps. Int. J. Artif. Intell. Tools 27(07), 1860010 (2018)

    Article  Google Scholar 

  22. Pelekis, N., Iakovidis, D.K., Kotsifakos, E.E., Kopanakis, I.: Fuzzy clustering of intuitionistic fuzzy data. Int. J. Bus. Intell. Data Min. 3(1), 45–65 (2008)

    Google Scholar 

  23. Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  24. Steel, R., Torrie, J., et al.: Principles and Procedures of Statistics. McGraw-Hill, New York (1960)

    Google Scholar 

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Acknowledgment

We acknowledge support of this work by the project “Smart Tourist” (MIS 5047243) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Program “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).

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Authors and Affiliations

  1. University of Thessaly, Papasiopoulou Street 2-4, 35131, Lamia, Greece

    Georgia Sovatzidi, Michael D. Vasilakakis & Dimitris K. Iakovidis

Authors
  1. Georgia Sovatzidi
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  2. Michael D. Vasilakakis
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  3. Dimitris K. Iakovidis
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Corresponding author

Correspondence to Dimitris K. Iakovidis .

Editor information

Editors and Affiliations

  1. University of Leeds, Leeds, UK

    Sharib Ali

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Fons van der Sommen

  3. University of Oxford, Oxford, UK

    Bartłomiej Władysław Papież

  4. Eindhoven University of Technology, Eindhoven, The Netherlands

    Maureen van Eijnatten

  5. University College London, London, UK

    Yueming **

  6. Eindhoven University of Technology, Eindhoven, The Netherlands

    Iris Kolenbrander

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Sovatzidi, G., Vasilakakis, M.D., Iakovidis, D.K. (2022). IF3: An Interpretable Feature Fusion Framework for Lesion Risk Assessment Based on Auto-constructed Fuzzy Cognitive Maps. In: Ali, S., van der Sommen, F., Papież, B.W., van Eijnatten, M., **, Y., Kolenbrander, I. (eds) Cancer Prevention Through Early Detection. CaPTion 2022. Lecture Notes in Computer Science, vol 13581. Springer, Cham. https://doi.org/10.1007/978-3-031-17979-2_8

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  • DOI: https://doi.org/10.1007/978-3-031-17979-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17978-5

  • Online ISBN: 978-3-031-17979-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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