Abstract: Structural Causal Models (SCMs) offer a powerful framework for understanding and reasoning about causal relationships, particularly in the context of total effects. In this presentation, we will explore a range of established tools for identifying total effects using fully specified Directed Acyclic Graphs (DAGs) derived from SCMs. These tools will be illustrated through two […]

Introduction to Weak Supervision & Applications The recent digitization of patient health records and their collection, in a near real-time basis, in Clinical Data Warehouses (CDWs) offer new perspectives for research, steering activities and policy making. Although promising, taking advantage of Electronic Health Records (EHR) is still a current challenge. Particularly, textual data are very […]

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 […]