Le centre privé de recherche et développement (R&D) Euranova, spécialisé dans la science des données et l’IA, et l’Institut Carnot CALYM annoncent une collaboration avec le soutien institutionnel de Roche pour faire avancer la recherche clinique en imagerie médicale, qui se concentrera sur le lymphome folliculaire.
IEEE Big Data 2025 signals a shift to secure, hybrid intelligence. CTO Sabri Skhiri unpacks the engineering reality from the conference: the practical shift to embeddings, the real need for security layers, and the limitations of AI agents in production.
The topic of explainable AI has recently received attention driven by a growing awareness of the need for transparent and accountable AI. In this paper, we propose a novel methodology to decompose any state-of-the-art perturbation-based explainability approach into four blocks. In addition, we provide Muppet: an open-source Python library for explainable AI.
We proposed a feature unlearning technique to reduce cancer-type bias, which improved segmentation accuracy while promoting fairness across sub-groups, even with limited data.