Jérôme Schmid

Jérôme Schmid

Professeur HES ordinaire

Technique en radiologie médicale IR-HEdS

  • Profil
  • Enseignements
  • Publications
  • Projets Ra&D

Contact

jerome.schmid@hesge.ch
+41 22 558 58 74

Haute école de santé - Genève
Avenue de Champel 47, 1206 Genève, CH

Présentation

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.

Liens

Orcid
ResearchGate
Scholar.google.fr

Compétences

Imagerie médicale Artificial Intelligence (AI) Traitement d'images Digital image processing Signal Processing (Images/Video) Computer Vision Machine Learning

Domaine : Santé
Filière principale : Technique en radiologie médicale

MSc HES-SO/UNIL en Sciences de la santé - HES-SO Master

Intelligence artificielle et médecine assistée par ordinateur

Acquisition et post-traitement des images radiologiques

Systèmes informatisés de santé

Mathématiques appliquées à l'imagerie radiologique

BSc HES-SO en Technique en radiologie médicale - Haute école de santé - Genève

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

46 publications

Characterization of fluid in facial sinuses on post-mortem CT in case of death by drowning

Mendes, L. F., Lago, L. P., Egger, C., Schmid, J.

(2025). Characterization of fluid in facial sinuses on post-mortem CT in case of death by drowning : International journal of legal medicine .

https://doi.org/10.1007/s00414-025-03493-3


Auteur(s) : Jérôme Schmid

Article scientifique

Learning MRI with ImmeRgaMe : exploring the pedagogical potential of an innovative serious game for radiographer training

Petit, M. A., Piguet, J., Lokaj, B., Schmid, J., Gaignot, C.

(2025). Learning MRI with ImmeRgaMe : exploring the pedagogical potential of an innovative serious game for radiographer training : Radiography.

https://doi.org/10.1016/j.radi.2025.102921


Auteur(s) : Marie-Anaïs Petit, Joël Piguet, Belinda Mataj-Lokaj, Jérôme Schmid, Céline Gaignot

Article scientifique

Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification

Lokaj, B., Durand de Gevigney, V., Djema, D. A., Zaghir, J., Goldman, J. P., Bjelogrlic, M., Turbe, H., Kinkel, K., Lovis, C., Schmid, J.

(2025). Multimodal deep learning fusion of ultrafast-DCE MRI and clinical information for breast lesion classification : Computers in biology and medicine.

https://doi.org/10.1016/j.compbiomed.2025.109721


Auteur(s) : Belinda Mataj-Lokaj, Jérôme Schmid, Valentin Durand De Gevigney

Article scientifique

Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review

Lokaj, B., Pugliese, M. T., Kinkel, K., Lovis, C., Schmid, J.

(2024). Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review : European radiology.

https://doi.org/10.1007/s00330-023-10181-6


Auteur(s) : Belinda Mataj-Lokaj, Marie-Therese Pugliese, Jérôme Schmid

Article scientifique

Barriers and facilitators of implementation of AI

Lokaj, B., Pugliese, M.-P., Kinkel, K., Lovis, C., Schmid, J.

(2024). Barriers and facilitators of implementation of AI : Annual Scientific Meeting 2024, European Society of Breast Imaging (EUSOBI).

https://www.eusobi.org/congress/


Auteur(s) : Belinda Mataj-Lokaj, Marie-Therese Pugliese, Jérôme Schmid

Présentation orale

Efficient clinical information extraction from breast radiology reports in French

Zaghir, J., Lokaj, B., Kinkel, K., Djema, A. D., Turbe, H., Bjelogrlic, M., Durand de Gevigney, V., Schmid, J., Lovis, C., Goldman, J. P.

(2024). Efficient clinical information extraction from breast radiology reports in French : Studies in health technology and informatics.

https://doi.org/10.3233/SHTI240776


Auteur(s) : Belinda Mataj-Lokaj, Valentin Durand De Gevigney, Jérôme Schmid

Chapitre de livre

Efficient clinical information extraction from breast radiology reports in French

Zaghir, J., Lokaj, B., Kinkel, K., Djema, A.-D., Turbé, H., Bjelogrlic, M., Durand De Gevigney, V., Schmid, J., Lovis, C., Goldman, J.-P.

(2024). Efficient clinical information extraction from breast radiology reports in French : Medical Informatics Europe.

https://mie2024.org/


Auteur(s) : Belinda Mataj-Lokaj, Valentin Durand De Gevigney, Jérôme Schmid

Présentation orale

Enhancing breast lesion classification by integrating lesion characteristics and clinical data information with ultrafast MRI

Lokaj, B., Durand de Gevigney, V., Goldman, J.-P., Kinkel, K., Lovis, C., Zaghir, J., Djema, D.-A., Schmid, J.

(2024). Enhancing breast lesion classification by integrating lesion characteristics and clinical data information with ultrafast MRI : European Congress of Radiology 2024 (EPOS).

https://congress.sgr-ssr.ch/


Auteur(s) : Belinda Mataj-Lokaj, Valentin Durand De Gevigney, Jérôme Schmid

Présentation orale

A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images

Schmid, J., Assassi, L., Chenes, C.

(2023). A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images : European radiology experimental.

https://doi.org/10.1186/s41747-023-00357-6


Auteur(s) : Jérôme Schmid, Christophe Chênes

Article scientifique

X-ray imaging detector for radiological applications adapted to the context and requirements of low- and middle-income countries

Chavarria, M. A., Huser, M. , Blanc, S. , Monnin, P. , Schmid, J., Chênes, C. , Assassi, L. , Blanchard, H., Sahli, R., Thiran, J. P. , Salathé, R. , Schönenberger, K.

(2022). X-ray imaging detector for radiological applications adapted to the context and requirements of low- and middle-income countries : Review of Scientific Instruments.

https://doi.org/10.1063/5.0077985


Auteur(s) : Jérôme Schmid, Christophe Chênes

Article scientifique