Applications of Belief Functions

  1. Huang, L., Xing, Y., Denœux, T., Feng, M. (2024). An Evidential Time-to-Event Prediction Model Based on Gaussian Random Fuzzy Numbers. In: Bi, Y., Jousselme, AL., Denoeux, T. (eds) Belief Functions: Theory and Applications. BELIEF 2024. Lecture Notes in Computer Science, vol 14909, pages 49-57, Springer, Cham, 2024. pdf
  2. Huang, L., Denoeux, T., Vera, P., Ruan, S. (2022). Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham. https://doi.org/10.1007/978-3-031-16443-9_39. pdf
  3. Xiaoqian Zhou, Xiaodong Yue, Zhikang Xu, Thierry Denoeux, and Yufei Chen. Deep Neural Networks with Prior Evidence for Bladder Cancer Staging. IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021), 2021. pdf
  4. L. Huang, T. Denoeux, D. Tonnelet, P. Decazes and S. Ruan. Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. In C. Lian et al. (Eds), Machine Learning in Medical Imaging, Springer International Publishing“, Cham, pages 30-39, 2021. pdf
  5. L. Huang, S. Ruan, P. Decazes and T. Denoeux. Evidential Segmentation of 3D PET/CT Images. In T. Denoeux, E. Lefèvre, Zh. Liu and F. Pichon (Eds), Belief Functions: Theory and Applications, Springer International Publishing, Cham, pages 159-167, 2021. pdf
  6. L. Huang, S. Ruan and T. Denoeux. Covid-19 Classification with Deep Neural Network and Belief Functions. Fifth International Conference on Biological Information and Biomedical Engineering (BIBE 2021), July 20–22, 2021, Hangzhou, China. pdf
  7. L. Huang, S. Ruan and T. Denoeux. Belief function-based semi-supervised learning for brain tumor segmentation. 2021 IEEE International Symposium on Biomedical Imaging (ISBI 2021), Nice, France, IEEE, 2021. pdf
  8. C. Lian, S. Ruan, T. Denoeux, Y. Guo and P. Vera. Accurate segmentation in FDG-PET images with guidance of complementary CT images. 2017 IEEE International Conference on Image Processing (ICIP 2017), Beijing, China, IEEE, 2017. pdf
  9. C. Lian, S. Ruan, T. Denoeux, H. Li and P. Vera. Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pages 1177-1180, Melbourne, Australia, IEEE, 2017. pdf
  10. C. Lian, S. Ruan, T. Denoeux, H. Li, and P. Vera, “Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images”, 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), Part II, LNCS 9901, Springer, Athens, Greece, pages 61-69, October 2016. pdf
  11. C. Lian, H. Li, T. Denoeux, P. Vera and S. Ruan, Dempster-Shafer Theory based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy, 18th International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI-2015), Part III, LNCS 9351, pages 695-702, Springer, Munich, Germany, October 2015. pdf
  12. C. Lian, S. Ruan, T. Denoeux and P. Vera. Outcome prediction in tumour therapy based on Dempster-Shafer Theory. IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015), New York, USA, pages 63-66, April 2015. pdf
  13. N. Sutton-Charani, S. Destercke and T. Denoeux. Application of E2M decision trees to rubber quality prediction. In A. Laurent, O. Strauss, B. Meunier et al. (Eds), 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU 2014), Montpellier, France, July 2014. In Information Processing and Management of Uncertainty in Knowledge-based Systems, Book Series: Communications in Computer and Information Science, Vol. 442, Pages 107-116, 2014. pdf
  14. N. El Zoghby, V. Cherfaoui and T. Denoeux. Evidential Distributed Dynamic Map for Cooperative Perception in VANets. In proceedings of IEEE intelligent Vehicles Symposium 2014, Dearborn, Michigan, USA, 8-11 juin 2014, pp. 1421-1426. pdf
  15. Z. L. Cherfi, L. Oukhellou, P. Akin and T. Denoeux. Using imprecise and uncertain information to enhance the diagnosis of a railway device. In S. Li et al. (Eds), Non Linear Mathematics for Uncertainty and its Applications, Beijing, China, pages 213-220, Septembre 2011. Advances in Intelligent and Soft Computing Vo. 100, Springer. pdf
  16. G. Nassreddine, F. Abdallah and T. Denoeux. A new method for state estimation of dynamic system based on Dempster-Shafer theory. In Proceedings of the Int. Conf. on Advances in Computational Tools for Engineering Applications (ACTEA '09), pages 101-106, Notre-Dame University, Lebanon, July 15-17 2009. pdf
  17. V. Cherfaoui, T. Denoeux and Z. L Cherfi. Distributed data fusion: application to confidence management in vehicular networks. In Proceedings of the 11th Int. Conf. on Information Fusion (FUSION '08), pages 846-853, Cologne, Germany, June 30-July 03, 2008. pdf
  18. G. Nassreddine, F. Abdallah and T. Denoeux. Map matching algorithm using belief function theory. In Proceedings of the 11th Int. Conf. on Information Fusion (FUSION '08), pages 995-1002, Cologne, Germany, June 30-July 03, 2008. pdf
  19. E. Côme, L. Oukhellou, P. Aknin and T. Denoeux. Diagnostic de systèmes spatialement répartis, modèle génératif et méthode à noyau. XXIe Colloque GRETSI, pages 633-636, September 2007, Troyes, France. pdf
  20. K. Worden, G. Manson and T. Denoeux. Evidence-based damage classification for an aircraft structure. Proceedings of First International Conference on Uncertainty in Structural Dynamics, pages 279-288, Sheffield, UK, 2007. pdf
  21. A. Debiolles, L. Oukhellou, T. Denoeux and P. Aknin. Output coding of spatially dependent subclassifiers in evidential framework. Application to the diagnosis of railway track-vehicle transmission system. Proceedings of FUSION'2006, Florence, Italy, July 2006. pdf
  22. W. Schön, T. Denoeux. Prise en compte des incertitudes dans les évaluations de risque à l'aide des fonctions de croyance. Congrès Maîtrise des risques et Sûreté de Fonctionnement, LambdaMu 14, Bourges, France, 12-14 Octobre 2004. pdf
  23. S. Démotier, W.Schön, T. Denoeux, K. Odeh. A new approach to assess risk in water treatment using the belief function framework. IEEE International Conference on Systems, Man and Cybernetics, 2003,Volume: 2 , 5-8 Oct. 2003, Vol. 2, Pages 1792 – 1797. pdf
  24. S. Démotier, T. Denoeux and W. Schön. Risk assessment in drinking water production using belief functions. In T. D. Nielsen and N. L. Zhang, Eds, Proceedings of ECQSARU'2003, pages 319-331, Aalborg, Denmark, July 2003, Springer-Verlag. postscript
  25. P. Vannoorenberghe et T. Denoeux. Diagnostic de la pollution atmosphérique par une approche RDF utilisant les fonctions de croyance. Colloque Automatique et Environnement A&E 2001, Saint-Etienne, 4-6 juillet 2001. pdf
  26. W. Schön, K. Odeh, T. Denoeux and F. Fotoohi. Maîtrise des risques dans le domaine de l'eau potable. In Actes du 12e Colloque National de Sûreté de Fonctionnement, pages 695-701, Montpellier, March 2000. ps, pdf