Le Limics porte un axe thématique qui concerne le Médicament. Cet axe utilise les approches d’ingénierie des connaissances et d’intelligence artificielle développées au Limics dans le cadre d’usage autour du médicament. Ce cas d’usage distingue (1) une recherche sur la formalisation des connaissances sur le médicament, ses propriétés et ses effets indésirables, (2) une recherche […]
MAESTRO will be an integrated pipeline for biomarker discovery and gene expression analysis, combining advanced imaging technologies with spatial transcriptomics (ST) and artificial intelligence (AI). The system will be designed to process data from various imaging modalities, such as ultrasound localization microscopy, to identify disease-specific molecular signatures and their spatial relationships and connection with gene […]
Eduardo Fermé, full professor of computer science at the Universidade da Madeira in Portugal, is spending one month as a visiting researcher at Limics. He is internationally recognized for his research in Artificial Intelligence, particularly in knowledge representation and the formalization of common-sense reasoning — key pillars of explainability in modern AI applications. Title: An […]
Title: Enhancing hematological image analysis with generalizable, interpretable, and adaptable AI models Lieu: Limics, Campus des Cordeliers, salle de conférence Mots-clés : Apprentissage profond,Leucocytes,Explicabilité,Généralisabilité,Classification,Semi-supervision Keywords: Deep Learning,White blood cells,Explainability,Generalisability,Classification,Semi-supervision Résumé : L’étude d’un échantillon sanguin peut être réalisée au moyen de plusieurs tests. Parmi ceux-ci, on appelle cytologie l’observation des cellules sanguines au microscope. L’objectif de […]
Thomas Papastergiou est maître de conférence au LIPN, USPN. Ses recherches portent sur l’intelligence artificielle et recouvrent une série d’applications biomédicales allant du diagnostic à la conception de médicaments nouvellement synthétisés. Tensor Decomposition for Multiple Instance Learning in Medical Applications Tensors are multidimensional data structures that naturally model multi-way medical data such as pathology images […]
Titre: Symbolic AI in the era of LLMs Abstract: Recent spectacular advances in Artificial Intelligence (AI) focus strongly on the sub-area of AI known as machine learning. In this talk, I will remind participants that there are several other sub-disciplines of AI, and that it is important for the field to maintain and grow expertise […]
Title: « Refining risk stratification using CMR in patients with hypertrophic cardiomyopathy ». Abstract: Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease, characterized by ventricular hypertrophy, myocyte disarray, and interstitial fibrosis, often resulting from sarcomere gene variants. Patients experience adverse events such as heart failure (HF), stroke, arrythmias and sudden cardiac death (SCD) leading to […]
Title: Use of Artificial Intelligence for the Analysis of Potential Pharmacovigilance Signals upon Real-World data Lieu: Limics, Campus des Cordeliers, salle de conférence Résumé : Cette thèse explore comment l’intelligence artificielle (IA), en particulier l’apprentissage automatique causal (CML), peut améliorer la pharmacovigilance en exploitant des données structurées issues du monde réel (RWD), telles que les dossiers […]