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For about a decade, a disease has been attracting the attention of the medical community. Its name: sarcopenia. This condition affects more than 10% of people over the age of 65 and is characterized by a significant loss of muscle mass and strength. Charlotte Beaudart, a member of the Department of Biomedical Sciences and the NARILIS Research Institute, has made a name for herself on the international stage in recent years by contributing to a better understanding of this disease and raising awareness about it.

How can women be better protected against the risks of thrombosis associated with the contraceptive pill? Researchers from the Department of Pharmacy at UNamur have just developed an innovative prediction model that can estimate the risk of thrombosis associated with taking combined oral contraceptives at an earlier stage. The ultimate goal is to support healthcare professionals in prescribing the right pill for each patient. 

Abstract Since its emergence in 1996, the Asian H5 Goose/Guangdong (Gs/Gd) lineage has circulated widely in poultry in southern China, spilling over to wild birds by 2002. Wild bird infections facilitated global dissemination via migratory waterfowl and repeated spillback into poultry, challenging the view that HPAI primarily arises from LPAI mutation. Subclade 2.3.4.4b emerged in Asia in 2013, reached Europe in 2016, caused recurrent epizootics, diversified into multiple genotypes, became dominant in wild birds, and shows zoonotic potential.This thesis investigates critical knowledge gaps regarding H5Nx subclade 2.3.4.4b in poultry: (1) early within-flock spread after punctual introduction in chickens, particularly during the first European epizootics; (2) influence of pre-existing immunity on silent circulation; (3) limitations in diagnostic throughput during epizootic peaks; (4) potential of environmental surveillance, including air and dust sampling; and (5) impact on egg contamination and the reproductive tract, relevant for food safety and zoonotic risk.Four main objectives were addressed: (1) development of an experimental model simulating punctual introductions and spread, comparing 2017 and 2020 strains and assessing pre-existing immunity; (2) enhancement of diagnostic capacity via alternative sampling, semi-automated RNA extraction, and high-throughput processing; (3) evaluation of air and dust sampling for virus monitoring under experimental and field conditions; and (4) assessment of egg contamination risk. Alternative sampling and environmental monitoring were also applied to Newcastle disease virus as a comparative notifiable pathogen. Jury Prof. Tuci Elio - University of Namur, BelgiumProf. Anthony Cleve - University of Namur, BelgiumProf. Pierre-Yves Schobbens - University of Namur, BelgiumProf. Alvaro Gutierrez - Universidad Politecnica de Madrid, EspagneMr. Fabian Duchesne - Qualitics SPRLProf. Anders Lyhne Christensen - SDU, Denmark Evènement public et gratuit - Inscription obligatoire Je m'inscris

To mark International Women's Day, celebrated on March 8, we invite you to discover the portraits of seven inspiring women from the university's seven faculties. Throughout March, a series of portraits of women from the university will be on display in various areas of the campus. Conceived and produced by four UNamur students and coordinated by the University Community Life Service (VéCU), this project offers an inspiring showcase for the careers, voices, and commitments of these women who bring the institution to life on a daily basis.  

On February 21, the University of Namur hosted the symposium "Medicine and Literature," organized by the History of Medicine and Health Sciences Group, with the support of the Royal Academy of French Language and Literature of Belgium. Nearly 70 participants gathered at the Faculty of Medicine for a particularly open and stimulating day of reflection, devoted to the deep and ancient links between medical practices and literary creation.

Abstract Deep learning has revolutionized computer vision in recent years and has been applied to many fields. This thesis focuses on medical endoscopy, where deep learning can assist physicians in many tasks, such as navigating the lungs during bronchoscopy, assisting in the detection of lung diseases, detecting Crohn's disease from capsule endoscopy (PillCam), or automating the detection of polyps during colonoscopy procedures.This thesis, entitled From Pixels to Practice: Deep Learning for Endoscopy, explores how modern neural networks and learning paradigms can improve visual understanding in endoscopy, with the aim of contributing to computer-aided detection (CAD) systems that can be integrated into clinical workflows.This work follows an article-based structure and links methodological advances in geometric and temporal modeling to techniques for handling data scarcity and imbalance, as well as to the practical and clinical implications of deep learning for lung tumor detection, both from a clinical and practitioner perspective. The first part of the manuscript provides a common foundation for all subsequent parts. First, we present a general introduction to the field of machine learning in Chapter 1, explaining concepts such as classification, loss functions, and artificial neural networks. Next, Chapter 2 focuses on the field of deep learning for computer vision, detailing the main vision tasks, the concept of convolutional neural networks, ResNet, and U-Net. Finally, Chapter 3 describes medical imaging, with a focus on computed tomography (CT) scans and optical imaging. The second part of the thesis focuses on learning spatio-temporal representations. In Chapter 4, we use deep neural networks combining spatial features and temporal recurrence to address the problem of detecting the bronchial carina, an anatomical landmark that helps doctors navigate the lungs. By evaluating classification (ResNet-50), segmentation (nnU-Net), and recurrent (GRU) models on a bronchoscopy dataset we created, the study highlights the benefits of combining information from segmentation masks and temporal features. Chapter 5 continues the segmentation task by analyzing the extent to which rotation-equivariant U-Nets, based on E(2)-CNNs with C4, C8, and D4 symmetry groups, can improve performance when the orientation of objects in the image is arbitrary. Together, these chapters show how temporal and geometric modeling capture complementary aspects of visual structure. They further highlight that data imbalance and scarcity are recurring problems in deep learning. The third part studies learning in situations of data scarcity and imbalance. First, Chapter 6 explores supervised contrastive pre-training [1] on large, domain-close endoscopic datasets (Hyper-Kvasir [2], LDPolyp [3]), which is then transferred to smaller, disease-specific data (Crohn-IPI [4]). This methodology performs better than pre-training on ImageNet or based on cross-entropy, highlighting the value of domain-specific contrastive representations. Next, Chapter 7 introduces Mask-Aware Cropping (MAC), a new data augmentation technique that mitigates pixel-level imbalance in segmentation. On various datasets with varying imbalance regimes (URDE [5], Kvasir-SEG [6], HAM10000 [7]), MAC consistently improves Dice and IoU metrics under conditions of extreme imbalance. Together, these methods form a data-centric framework for effective learning when annotations are scarce or unevenly distributed. The fourth part of the thesis focuses on deep learning in the operating room. Chapter 8 proposes a first model (ResNet-50) for the visual detection of lung cancer in bronchoscopy, trained on real, in-vivo data. The model outperforms junior physicians, while remaining inferior to experts. This result shows that CAD systems for lung cancer detection are promising. Chapter 9 extends this work by evaluating the usability of a CAD system based on a deep learning model. Combining probability indices, temporal graphs, and saliency map overlays, a multicenter evaluation with 10 physicians is conducted. The tool received favorable feedback, with high usability (SUS score of 80.5 [8]) and strong clinical acceptance. Beyond endoscopy, the results concerning rotation equivariance and pixel imbalance can be generalized to other fields such as microscopy, dermatology, and aerial imaging. This shows that the proposed methods are applicable to visual learning under structured variability and limited data constraints.Keywords: machine learning, computer vision, medicine, endoscopy, convolutional neural networks, segmentation, recurrent models, equivariance.  Jury Prof. Bruno Dumas - University of NamurProf. Frénay Benoit - University of NamurProf. Schobbens P-Y. - University of NamurProf. Beuls Katrien - University of Namur,Dr. Benjamin Mertens - Lys MédicalProf. Oramas Mogrojevo José Antonio - University of AntwerpDr. Mancas Matei - University of Mons Register

May 5, 2026 | Registration for Thibaut Septon’s public thesis defense Name First name E-mail address Will attend the reception following the defense Yes ( optional ) No ( optional ) Need a parking sticker Yes ( optional ) No ( optional ) Would like a certificate for defense assistance Yes ( optional ) No ( optional ) In order to process your request, you must complete all fields marked "optional". When you submit this form, the completed data will be transmitted to UNamur and used to process your request. Learn more about your data protection and your rights This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

At UNamur, parchments are much more than objects of curiosity: they are at the heart of an interdisciplinary scientific adventure. Starting with historical sciences and conservation, the research has gradually incorporated the disciplines of physics, biology, chemistry, and archaeology.  This convergence has given rise to research in heritage sciences, driving innovative projects such as Marine Appart's doctoral work, supervised by Professor Olivier Deparis. This research has now been recognized with a publication in the prestigious journal Heritage Science (Nature Publishing Group).

At a time when misinformation, post-truths and conspiracies are undermining confidence in science, UNamur welcomes SPiN (Science & Philosophy in Namur), a new interdisciplinary research center that questions the place of science in society. Founded last September by Olivier Sartenaer, Professor of Philosophy of Science at UNamur, SPiN brings together philosophers and scientists around a common vision: to develop a critical and accessible reflection on science in all its diversity..

The lack of primary care is a major public health issue. In 2022, it was estimated that 52 municipalities in French-speaking Belgium were facing a severe shortage of general practitioners. This is a worrying situation that the University Observatory for Rural Medicine (OUMRu) has been addressing since 2023, with the aim of identifying concrete solutions. Working alongside a doctor and a health geographer, Amélie Pierre, a sociologist and lecturer at the Faculty of Economics, Management and Communication SciencesPo (EMCP), is studying the factors that influence access to healthcare, particularly from the patients' point of view. She emphasizes the need to take into account the realities experienced by vulnerable groups.

JuryProf. Guillaume BERIONNI (UNamur), PresidentProf. Stéphane VINCENT (UNamur), SecretaryProf. Carmen GALAN (University of Bristol)Dr. Louis FENSTERBANK (Collège de France)Prof. Raphaël ROBIETTE (Université catholique de Louvain)AbstractCarboxylic acids are ubiquitous in nature and inexpensive compounds. Decarboxylation has become a key chemical transformation and has been widely reported in organic chemistry except for carbohydrates. This reaction can be catalyzed by transition metal and can also be induced by light, thermal activation, or photocatalysis. Borylated compounds have stimulated the pharmaceutical industry's interest (Boromycin, Bortezomib or boron neutron capture therapy). Recent methodologies have been developed to transform carboxylic acids to boronate esters by metal-catalyzed or light-promoted or photocatalyzed reactions. In this thesis, we explored the synthesis of borylated carbohydrates through a decarboxylation pathway. More specifically, sialic acids being among the most important carbohydrates in glycobiology, we addressed the problem of the synthesis of borylated sialic acids. On the other hand, organophosphates play an important role in diverse fields: in materials chemistry, in agriculture, in organic chemistry, and in biochemistry. Phosphorylation is a key reaction in biological processes such as signal transduction and cell activity regulation. The formation of phosphorylated carbohydrates has been widely described through two-electron mechanisms. However, radical phosphorylation of carbohydrates remains unexplored. This Ph.D. thesis describes the development of new methodologies for the decarboxylative functionalization of carbohydrates, focusing on borylation and phosphorylation..

On December 4, second-year students in the Master's program specializing in general medicine at UNamur enjoyed a unique training day. They had the opportunity to interact directly with residents of Château Vert, an institution that welcomes people with disabilities. The goal was to better understand their expectations of general practitioners and improve the quality of medical care for these patients with specific needs.