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Stassin Sédrick

Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle

Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle

ORCID
0000-0001-5179-9623
Main Referenced Co-authors
MAHMOUDI, Sidi  (10)
SIEBERT, Xavier  (8)
AMEL, Otmane  (6)
Daho, Mostafa El Habib (2)
De Vleeschouwer, Christophe (2)
Main Referenced Keywords
Deep Learning (5); Artificial Intelligence (3); Explainable Artificial Intelligence (3); XAI (3); HS Code Prediction (2);
Main Referenced Unit & Research Centers
CRTI - Centre de Recherche en Technologie de l'Information (2)
Main Referenced Disciplines
Computer science (12)
Electrical & electronics engineering (1)
Radiology, nuclear medicine & imaging (1)

Publications (total 12)

The most downloaded
142 downloads
Stassin, S., Amel, O., Mahmoudi, S., & Siebert, X. (2023). Similarity versus Supervision: Best Approaches for HS Code Prediction. In ESANN 2023 proceedings (31st). i6doc.com. https://hdl.handle.net/20.500.12907/47896

The most cited

8 citations (Scopus®)

Mahmoudi, S., Stassin, S., Daho, M. E. H., Lessage, X., & Mahmoudi, S. (2021). Explainable Deep Learning for Covid-19 Detection Using Chest X-ray and CT-Scan Images. In Intelligent Healthcare Informatics for Fighting the COVID-19 and Other Pandemics and Epidemics (pp. 323-338). Springer. https://hdl.handle.net/20.500.12907/42412

Stassin, S.* , ENGLEBERT, A.* , Amel, O.* , Siebert, X., & Mahmoudi, S. (29 May 2024). Explaining Through Multimodal Transformer Input Sampling [Paper presentation]. Infortech Day 2024, Mons, Belgium.
Editorial reviewed
* These authors have contributed equally to this work.

Stassin, S., Corduant, V., MAHMOUDI, S., & Siebert, X. (30 December 2023). Explainability and Evaluation of Vision Transformers: An In-Depth Experimental Study. Electronics, 13 (1), 175. doi:10.3390/electronics13010175
Peer Reviewed verified by ORBi

Stassin, S., Amel, O., Siebert, X., & Mahmoudi, S. (24 May 2023). Best Approaches for Customs Fraud Detection [Paper presentation]. Infortech Day.

Stassin, S., Englebert, A., Nefack, G., Albert, J., Versbraegen, N., Peiffer, G., Doh, M., Riche, N., Frenay, B., & De Vleeschouwer, C. (2023). An Experimental Investigation into the Evaluation of Explainability Methods. Communications in Computer and Information Science.
Peer reviewed

Stassin, S., Amel, O., Mahmoudi, S., & Siebert, X. (2023). Similarity versus Supervision: Best Approaches for HS Code Prediction. In ESANN 2023 proceedings (31st). i6doc.com.
Peer reviewed

Englebert, A., Stassin, S., Nanfack, G., Mahmoudi, S., Siebert, X., Cornu, O., & De Vleeschouwer, C. (2023). Explaining through Transformer Input Sampling. In Proceedings of the IEEE/CVF International Conference on Computer Vision. IEEE Xplore. doi:10.1109/ICCVW60793.2023.00088
Peer reviewed

Amel, O., Stassin, S., Mahmoudi, S., & Siebert, X. (2023). Multimodal Approach for Harmonized System Code Prediction. In ESANN 2023 proceedings (31st). i6doc.com.
Peer reviewed

Stassin, S., Versbraegen, N., Nanfack, G., Jean, E., & Duboquet, F. (September 2022). Bias Detection On Image Data and their Mitigation on Models [Paper presentation]. TRAIL Workshop 2022.

Amel, O., Mahmoudi, S., Siebert, X., & Stassin, S. (30 March 2022). Review of multimodal approaches [Paper presentation]. Infortech day on Data science, Mons, Belgium.

Stassin, S.* , Amel, O., Mahmoudi, S., & Siebert, X. (30 March 2022). Artificial Intelligence and Natural Language Processing for Customs Fraud Prediction [Paper presentation]. InforTech Day, Mons, Belgium.
Editorial reviewed

Mahmoudi, S., Stassin, S., Daho, M. E. H., Lessage, X., & Mahmoudi, S. (2021). Explainable Deep Learning for Covid-19 Detection Using Chest X-ray and CT-Scan Images. In Intelligent Healthcare Informatics for Fighting the COVID-19 and Other Pandemics and Epidemics (pp. 323-338). Springer.

Mahmoudi, S., Stassin, S., Daho, M. E. H., Lessage, X., & Mahmoudi, S. (2021). Explainable Deep Learning for Covid-19 detection using chest X-ray and CT images. In Intelligent Healthcare Informatics for Fighting the COVID-19 and Other Pandemics and Epidemics. Springer. doi:10.1007/978-3-030-72752-9_16
Peer reviewed

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