Bidossessi Emmanuel Agossou
About
Innovative, problems solver, results oriented and Teams player, I received with Distinction a MSc. Master degree in Computer Science (from Université de Namur - UNamur), Belgium in 2019 after obtaining with Distinction an Engineer degree in Computer Science and Telecommunications(from Ecole Polytechnique d'Abomey-Calavi, UAC, Benin) in 2011. I also hold a Bachelor degree in English- Major African Studies from the University of Abomey-Calavi in 2008.
From 2012, I have worked in several institutions (both private and public) as computer analyst, Software developer, programmer, consultant and part-time lecturer in some private universities in Cotonou (Benin).
I have also worked in Benin national ministry of defense as computer system analyst and designer for three(3) and half years from 2015 to 2018.
I have finished my second master courses in Information Systems at Kobe Institute of Computing(KIC) in Japan in 2021.
I worked as Country manager for Benin in Dots For Inc.
Currently, I am also a PhD Student in Colourlab at NTNU (Norwegian University of Science and Technology), Department of Computer Science at Gjovik Campus, Norway. I am researching on medical imaging (Capsule Endoscopy) and pathology classification/detection using deep learning.
Research
My research is about designing / implementing Deep Learning (AI) models to improve medical images analysis and pathology detection and segmentation in order to help health practitionners to go faster with the diagnosis.
Currently I am working on semi-supervised AI methods to better detect (using bounding boxes) or segment (using masks) lesions (pathologies) inside medical images under limited annotations. My current application field is Capsule Endoscopy. The designed method can also be easily adpated to other modality medical images such as CT, MRI..
Publications
- July 2025: Bidossessi Emmanuel Agossou, Marius Pedersen, Kiran Raja, and Vats Anuja. 2025. Improving pseudo-labels using the embeddings for semi-supervised pathology detection in Capsule Endoscopy. (Journal paper) ACM Transactions on Multimedia Computing, Communications, and Applications, Special Issue on Advancing Medical Segmentation Through Emerging Deep Learning Architectures and Large Models [Under Review].
- May 2025: Bidossessi Emmanuel Agossou, Marius Pedersen, Kiran Raja, and Vats Anuja. 2025. Improving pseudo-labels selection using domain priors for semi-supervised detection in capsule endoscopy. In IEEE International Conference on Image Processing [Accepeted for ICIP 2025]. IEEE.
- December 2024 : AGOSSOU, Bidossessi Emmanuel, PEDERSEN, Marius, RAJA, Kiran, et al. Influence of color correction on pathology detection in Capsule Endoscopy. In : International Conference on Pattern Recognition. Cham : Springer Nature Switzerland, 2024. p. 365-379.
2025
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Agossou, Bidossessi Emmanuel;
Pedersen, Marius;
Raja, Kiran;
Vats, Anuja;
Floor, Pål Anders.
(2025)
Influence of Color Correction on Pathology Detection in Capsule Endoscopy.
Academic chapter/article/Conference paper
-
Agossou, Bidossessi Emmanuel;
Pedersen, Marius;
Vats, Anuja;
Raja, Kiran.
(2025)
Improving Pseudo-Labels Selection Using Domain Priors for Semi-Supervised Detection in Capsule Endoscopy.
Proceedings of IEEE International Conference on Image Processing
Academic article
Journal publications
-
Agossou, Bidossessi Emmanuel;
Pedersen, Marius;
Vats, Anuja;
Raja, Kiran.
(2025)
Improving Pseudo-Labels Selection Using Domain Priors for Semi-Supervised Detection in Capsule Endoscopy.
Proceedings of IEEE International Conference on Image Processing
Academic article
Part of book/report
-
Agossou, Bidossessi Emmanuel;
Pedersen, Marius;
Raja, Kiran;
Vats, Anuja;
Floor, Pål Anders.
(2025)
Influence of Color Correction on Pathology Detection in Capsule Endoscopy.
Academic chapter/article/Conference paper