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Jules Collenne - AI Researcher
Jules Collenne is a PhD candidate in Artificial Intelligence applied to Medicine, specializing in computer-aided diagnosis, machine learning interpretability, and unsupervised visual representation learning.
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Posts
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Blog Post number 1
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portfolio
FoodNow
Recipe android app to help amateur to discover new recipes and cooking techniques.
Marmodio
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.
NoRiz
A pacman-like videogame in the theme of sushis and cats !
publications
Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions
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
Enhancing Anomaly Detection in Melanoma Diagnosis Through Self-Supervised Training and Lesion Comparison
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
Fusion between an Algorithm Based on the Characterization of Melanocytic Lesions Asymmetry with an Ensemble of Convolutional Neural Networks for Melanoma Detection
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
Automated melanoma detection. An algorithm inspired from human intelligence characterizing disordered pattern of melanocytic lesions improving a convolutional neural network.
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
ReSet: A Residual Set-Transformer approach to tackle the ugly-duckling sign in melanoma detection
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.
talks
teaching
Communication
Undergraduate course, Aix-Marseille University, BUT Réseaux et Télécommunication, 2023
Data Sources with SQL
Undergraduate course, Aix-Marseille University, BUT Réseaux et Télécommunication, 2023
Web development
Undergraduate course, Aix-Marseille University, BUT Réseaux et Télécommunication, 2024