Alaedine is a new PhD student at Limics, and will present his research subject, as well as a focus on synthetic health data. This presentation explores the role of synthetic data as a promising solution to overcome regulatory barriers in healthcare machine learning. By detailing its methodology, including regulatory compliance, reduced re-identification risks, and usability […]

Title: How can we use hierarchical classification to improve the representation of white blood cell images by foundation models? Summary: In this presentation I will first make an introduction about what is hierarchical classification and how can it be implemented in Deep Learning. I will also introduce DinoV2 architecture that is current state-of-the-art foundation model […]

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 […]