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Applications of belief functions

Medical image processing

  1. Xiaoqian Zhou, Xiaodong Yue, Zhikang Xu, Thierry Denoeux, Yufei Chen. PENet: Prior Evidence Deep Neural Network for Bladder Cancer Staging. Methods, vol. 207, pages 20–28, 2022. pdf
  2. Ling Huang, Su Ruan, Pierre Decazes and Thierry Denoeux. Lymphoma segmentation from 3D PET-CT images using a deep evidential network. International Journal of Approximate Reasoning, Volume 149, pages 39-60, 2022. pdf
  3. C. Lian, S. Ruan, T. Denoeux, H. Li and P. Vera. Joint Tumor Segmentation in PET-CT Images using Co-Clustering and Fusion based on Belief Functions. IEEE Transactions on Image Processing, vol. 28, Issue 2, pages 755-766, 2019. pdf
  4. C. Lian, S. Ruan, T. Denoeux, H. Li and P. Vera. Spatial Evidential Clustering with Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images. IEEE Transactions on Biomedical Engineering, Volume 65, Issue 1, pages 21-30, 2018. pdf
  5. C. Lian, S. Ruan, T. Denoeux, F. Jardin, P. Vera. Selecting Radiomic Features from FDG-PET Images for Cancer Treatment Outcome Prediction. Medical Image Analysis, Volume 32, Pages 257-268, 2016. pdf
  6. B. Lelandais, S. Ruan, T. Denoeux, P. Vera, I. Gardin. Fusion of multi-tracer PET images for Dose Painting. Medical Image Analysis, Volume 18, Issue 7, Pages 1247-1259, 2014. pdf

Other applications

  1. Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet-Cuong Nguyen, Takahiro Nagata, Toyohiro Chikyow, Hiori Kino, Takashi Miyake, Thierry Denoeux, Van-Nam Huynh and Hieu-Chi Dam. Evidence-based recommender system for high-entropy alloys. Nature Computational Science, 2021. https://www.nature.com/articles/s43588-021-00097-w.
  2. L. Sui, P. Feissel and T. Denoeux. Identification of Elastic Properties in the Belief Function Framework. International Journal of Approximate Reasoning, vol. 101, pages 69-87, 2018. pdf
  3. J.-B. Bordes, F. Davoine, Ph. Xu and T. Denoeux. Evidential Grammars: A Compositional Approach For Scene Understanding. Application To Multimodal Street Data. Applied Soft Computing, Volume 61, Pages 1173-1185, 2017. pdf
  4. Ph. Xu, F. Davoine, J.-B. Bordes, H. Zhao and Th. Denoeux. Multimodal Information Fusion for Urban Scene Understanding. Machine Vision and Applications, Volume 27, Issue 3, pp 331-349, 2016. pdf
  5. Ph. Xu, F. Davoine, J.-B. Bordes, T. Denoeux. Fusion d'informations pour la compréhension de scènes. Traitement du Signal, Vol. 31, Number 1-2, pages 57-80, 2014. pdf
  6. Z. L. Cherfi, L. Oukhellou, E. Côme, T. Denoeux and P. Aknin. Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: Application to railway track circuit diagnosis. Soft Computing, Vol. 16, Number 5, pages 741-754, 2012. pdf
  7. F. Abdallah, G. Nassreddine and T. Denoeux. A multiple-hypotheses map matching method suitable for weighted and box-shaped state estimation for localization. IEEE Transactions on Intelligent Transportation Systems, Vol. 12, Issue 4, pages 1495-1510, 2011. pdf
  8. L. Oukhellou, A. Debiolles, T. Denoeux and P. Aknin. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion. Engineering Applications of Artificial Intelligence, Vol. 23, pages 117-128, 2010. pdf
  9. K. Worden, G. Manson, T. Denoeux. An evidence-based approach to damage location on an aircraft structure. Mechanical Systems and Signal Processing, Vol. 23, Issue 6, pages 1792-1804, 2009. pdf
  10. D. Mercier, G. Cron, T. Denoeux and M.-H. Masson. Decision fusion for postal address recognition using belief functions. Expert Systems with Applications, Vol. 36, Issue 3, pages 5643-5653, 2009. pdf
  11. D. Mercier, G. Cron, T. Denoeux and M.-H. Masson. Fusion de décisions postales dans le cadre du Modèle des Croyances Transférables. Traitement du Signal, Vol. 24, Issue 2, pages 133-151, 2007. pdf
  12. S. Démotier, W. Schön and T. Denoeux. Risk Assessment Based on Weak Information using Belief Functions: A Case Study in Water Treatment, IEEE Transactions on Systems, Man and Cybernetics C, , Vol. 36, Issue 3, 382-396, 2006. postscript, pdf

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