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Hedi YAZID

Hedi YAZID

DaSSIP Team, Associate Professor

Hedi YAZID is currently an Associate Professor in Computer Science at Isep.
He is a member of the DASSIP team within the LISITE laboratory. He obtained his PhD in Computer Science in November 2014 from the University of Sfax, Tunisia, and his Master’s degree in Management Information Systems in 2006 from the University of Tunis. He has been an Associate Researcher at IMT Atlantique Bretagne-Pays de Loire since 2014 and was an Associate Professor at the University of Monastir, Tunisia (2011–2022).
His current research focuses on computer-assisted decision-making in the medical domain and knowledge discovery using data mining and machine learning techniques. His work also addresses applications of indexing and search in the context of Big Data.

01 49 54 52 92
hedi.yazid@isep.fr

Search interests:

- Computer Vision
- Medical image processing
- Machine learning/deep learning applied to knowledge mining.
- Content based reasoning

Journals:

  • Asma Daly, Hedi Yazid, Basel Solaiman, Najoua Essoukri Ben Amara, “Multiatlas-based segmentation of female pelvic organs: Application for computer-aided diagnosis of cervical cancer”. International Journal of Imaging Systems Technologies 31(1): 302-312 (2021)
  • Hedi Yazid, Karim Kalti, Najoua Essoukri Ben Amara, “A similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval”, International Journal of Computational Intelligence Systems.
  • Asma Daly, Hedi Yazid, Basel Solaiman, Najoua Essoukri Ben Amara: Multi-atlas based segmentation of human cerebellum”. SSD 2020.
  • Sonia Mesbahi, Hedi Yazid: Automatic segmentation of medical images using convolutional neural networks”. ATSIP 2020.
  • Asma Daly, Hedi Yazid, Basel Solaiman, Najoua Essoukri Ben Amara: Multimodal Medical Image Registration Based on a Hybrid Optimization Strategy”. ATSIP 2020.
  • Hedi Yazid, Karim Kalti, Najoua Essoukri Ben Amara, A Performance Comparison of the Bayesian Graphical Model and the Possibilistic Graphical Model Applied in a Brain MRI Cases Retrieval Contribution. SSD 2013, Mars 2013.
  • Hedi Yazid, Karim Kalti, Fatma Elouni and Najoua Essoukri Ben Amara: A Probabilistic Network Based Similiarity measure for Cerebral Tumors MRI Cases Retrieval. IEEE Symposium Series on Computational Intelligence, SSCI 2011, Avril 2011, Paris, France.

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