FoodNow
Recipe android app to help amateur to discover new recipes and cooking techniques.
Recipe android app to help amateur to discover new recipes and cooking techniques.
A project made during my internship at the Institut de Neurosciences de la Timone along with Charly Lamothe and Pascal Belin. It is a machine learning-based software to denoise, detect, and extract marmoset or other monkey sounds. Once extracted, the sounds can be classified in a semi-supervised manner by experts.
A pacman-like videogame in the theme of sushis and cats !
Published in International Journal of Molecular Sciences, 2022
A game theory approach to analyse dermatological images of skin lesions.
Recommended citation: A. C. Foahom Gouabou, J. Collenne, J. Monnier, R. Iguernaissi, J.-L. Damoiseaux, A. Moudafi, D. Merad."Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions." International Journal of Molecular Sciences. 2022; 23(22) https://www.mdpi.com/1937126
Published in Machine Learning in Medical Imaging, 2023
A new framework for entirely unsupervised anomaly detection in the field of skin lesion analysis. Our approach leverage self-supervised CNNs and an unsupervised anomaly detection algorithm to detect melanomas without any annotation.
Recommended citation: J. Collenne, R. Iguernaissi, S. Dubuisson, D. Merad (2024). "Enhancing Anomaly Detection in Melanoma Diagnosis Through Self-Supervised Training and Lesion Comparison." Machine Learning in Medical Imaging. MLMI 2023. Vancouver, BC, Canada. https://link.springer.com/chapter/10.1007/978-3-031-45676-3_16
Published in Journal of Investigative Dermatology, 2024
We develop an approach focusing on the asymmetry within skin lesions to detect melanomas. An asymmetry model predicts the asymmetry rate of a given lesion, which, when combined with convolutional neural networks, improves the final results and enhances interpretability for dermatologists.
Recommended citation: J. Collenne, J. Monnier, R. Iguernaissi, M. Nawaf, M.-A. Richard, J.-J. Grob, C. Gaudy-Marqueste, S. Dubuisson, D. Merad. (2024). "Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection." Journal of Investigative Dermatology. https://doi.org/10.1016/j.jid.2023.09.289
Published in Journal of the American Academy of Dermatology, 2024
An approach focused on the measure of disorder within skin lesions using machine learning models.
Recommended citation: J. Monnier, A. C. Foahom Gouabou, M. Serdi, J. Collenne, R. Iguernaissi, M.-A. Richard, C. Gaudy-Marqueste, J.-L. Damoiseaux, J.-J. Grob, D. Merad. (2024). "Automated melanoma detection. An algorithm inspired from human intelligence characterizing disordered pattern of melanocytic lesions improving a convolutional neural network." Journal of the American Academy of Dermatology. https://doi.org/10.1016/j.jaad.2024.02.063
Published in 2024 IEEE International Conference on Image Processing (ICIP), 2024
We introduce ReSeT (Residual Set-Transformer), a novel approach to address the Ugly-Duckling Sign in melanoma detection. By comparing skin lesions within patients, our model effectively increases accuracy while remaining simple and lightweight, making it generalizable for other applications.
Recommended citation: J. Collenne, R. Iguernaissi, S. Dubuisson, D. Merad (2024). "ReSet: A Residual Set-Transformer approach to tackle the ugly-duckling sign in melanoma detection." International Conference on Image Processing. ICIP 2024. Abu Dhabi, UAE.
Undergraduate course, Aix-Marseille University, BUT Réseaux et Télécommunication, 2023
Undergraduate course, Aix-Marseille University, BUT Réseaux et Télécommunication, 2023
Undergraduate course, Aix-Marseille University, BUT Réseaux et Télécommunication, 2024