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Professeur HES ordinaire
Technique en radiologie médicale IR-HEdS
jerome.schmid@hesge.ch
+41 22 558 58 74
Haute école de santé - Genève
Avenue de Champel 47, 1206 Genève, CH
Prof. Jérôme Schmid has been working for many years in medical image processing and understanding, focusing on segmentation and registration. He specialized in physically-based deformable models applied to the modeling of the human musculoskeletal system. Prof. Schmid also explored the combination of deformable models with machine learning, as well as the use of deep learning for computer-assisted diagnosis such as bone fracture recognition in wrist radiographs, Parkinson’s disease detection in SPECT imaging or breast lesion detection in dynamic MRI. Application of artificial intelligence for pedagogic purpose were also explored by his team when devising the AIRx training prototype to simulate the realistic generation of radiographs.
Since 2011, Prof. Schmid and his team at HES-SO have built up a strong expertise in computer-assisted diagnosis and image-guided surgery in several research projects funded by public and private sectors, including the Swiss National Science Foundation, the Swiss Innovation Agency and various swiss foundations.
Orcid
ResearchGate
Scholar.google.fr
Domaine :
Santé
Filière principale :
Technique en radiologie médicale
Intelligence artificielle et médecine assistée par ordinateur
Acquisition et post-traitement des images radiologiques
Systèmes informatisés de santé
Mathématiques appliquées à l'imagerie radiologique
Propriétés de l'image numérique
Reconstruction, traitement et visualisation d'images radiologiques
Etude de cas multimodalités
Méthodologie de recherche
Intelligence artificielle et médecine assistée par ordinateur
Reconstruction, traitement et visualisation d'images radiologiques
Cardoso, B., Laghmich, C., Monnard, N, Pichon, S., Schmid, J.
(2026). A serious game to support radiographers’ learning of artificial intelligence fundamentals : Swiss Congress of Radiology.Auteur(s) : Swann Pichon, Jérôme Schmid
Durand de Gevigney, V., Lokaj, B., Lovis, C., Djema, A. D., Kinkel, K., Schmid, J.
(2026). Clinical evaluation of an AI-based prototype for breast lesion classification in ultrafast dynamic contrast enhanced MRI : Swiss Congress of Radiology.Auteur(s) : Belinda Mataj-Lokaj, Jérôme Schmid
Diop, A. T., Mazzini, B., Ledoux, J.-B., Schmid, J., Dunet, V.
(2026). Feasibility of high b-value for the detection of ischemic stroke in the posterior fossa: head-to-head comparison with standard b-value : Swiss Congress of Radiology.Auteur(s) : Jérôme Schmid
Schmid, J., Al-Musibli, A., Cagdas, O., Foufi, V., Bjelogrlic, M., Lovis, C., Poletti, P.-A., Montet, X., Bouredoucen, H., Boudabbous, S.
(2026). Investigating the use of artificial intelligence to detect scaphoid fractures from a single incidence radiograph : Computer methods in biomechanics and biomedical engineering : imaging & visualization.https://doi.org/10.1080/21681163.2026.2634726
Auteur(s) : Jérôme Schmid, Azal Al-Musibli
Chênes, C, Ardaine, M., Carrillo, C., Youssef, E., Schmid, J.
(2026). Investigating the use of generative artificial intelligence for shoulder radiography education : European Congress of Radiology (EPOS).https://dx.doi.org/10.26044/ecr2026/C-22707
Auteur(s) : Christophe Chênes, Jérôme Schmid
Chênes, C, Ardaine, M., Carillo, C. , Youssef, E. , Schmid, J.
(2026). Investigating the use of generative artificial intelligence for shoulder radiography education : European Society of Radiology.https://dx.doi.org/10.26044/ecr2026/C-22707
Auteur(s) : Jérôme Schmid
Petit, M. A., Dubois, M., Dietrich, T., Cooper, A., Schmid, J., Enoch, S., Bechevet, D., Abdeddaim, R.
(2026). Mechanically adjustable inductive impedance matching of a low-field 0.1 T MRI solenoid : IEEE Access.https://doi.org/10.1109/access.2026.3683548
Auteur(s) : Marie-Anaïs Petit, Ashley Cooper, Jérôme Schmid
Durand de Gevigney, V., Nicastro, N., Mathoux, G., Marion, L., Page, C. S., Garibotto, V, Schmid, J.
(2026). Multimodal deep learning with SPECT imaging and striatal features for detecting degenerative parkinsonisms : European Congress of Radiology (EPOS).https://dx.doi.org/10.26044/ecr2026/C-21138
Auteur(s) : Jérôme Schmid
Durand De Gevigney, V., Nicastro, N., Mathoux, G. , Marion, L., Page, C. S., Garibotto, V., Schmid, J.
(2026). Multimodal deep learning with SPECT imaging and striatal features for detecting degenerative parkinsonisms : European Society of Radiology'.https://dx.doi.org/10.26044/ecr2026/C-21138
Auteur(s) : Jérôme Schmid
Durand de Gevigney, V., Nicastro, N., Garibotto, V., Schmid, J.
(2026). Multimodal fusion and transfer learning for the detection of degenerative parkinsonisms with dopamine transporter SPECT imaging : Journal of imaging informatics in medicine .https://doi.org/10.1007/s10278-025-01831-w
Auteur(s) : Jérôme Schmid
