List of Publications
Détection automatisée du mélanome fondée sur l’analyse cognitive globale du dermatologue et comparaison aux approches neuronales. Annales de Dermatologie et de Vénéréologie - FMC, Volume 4, Issue 8, Supplement 1, 2024, Page A98. ISSN 2667-0623.
Forecasting Empty Container availability for Vehicle Booking System Application. Proceedings of the 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), 2024.
Towards a Digital Twin Modeling Method for Container Terminal Port. Proceedings of the 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES), 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, 2024.
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.
End-to-End Decoupled Training: A Robust Deep Learning Method for Long-Tailed Classification of Dermoscopic Images for Skin Lesion Classification. Electronics, 2022.
Rethinking decoupled training with bag of tricks for long-tailed recognition. Digital Image Computing: Techniques and Applications (DICTA), 2022.
Automated detection of melanoma: Comparing a convolutional neural network (CNN) approach with an algorithm assessing disorder in the pattern of pigmented lesions, intended to mimick onco-dermatologists' visual analysis. Annals of Oncology, 2022.
Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application. Sensors, 2021.
Détection automatique du mélanome: comparaison d’un algorithme fondé sur la caractérisation de l’aspect désordonné de lésions mélanocytaires mimant la pratique des dermatologues, avec une approche par CNN (Convolutional Neural Network). Annales de Dermatologie et de Vénéréologie-FMC, 2021.
Impact d’un artefact fréquent sur la détection automatique du mélanome à partir d’images dermoscopiques: approche deep learning combinée à l’algorithme Support Vector Machine.Annales de Dermatologie et de Vénéréologie, 2020.