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Professeur HES ordinaire
Technique en radiologie médicale
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
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
Lokaj, B., Pugliese, M. T., Kinkel, K., Lovis, C., Schmid, J.
(2023). 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
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
D’Isidoro, F., Chênes, C., Ferguson, S.J., Schmid, J.
(2021). A new 2D-3D registration gold-standard dataset for the hip joint based on uncertainty modeling : Medical Physics .https://doi.org/10.1002/mp.15124
Auteur(s) : Jérôme Schmid, Christophe Chênes
Schmid, J. , Chênes, C.
(2021). Revisiting contour-driven and knowledge-based deformable models : application to 2D-3D proximal femur reconstruction from X-ray images : Proceedings of Medical Image Computing and Computer Assisted Intervention – MICCAI 2021.http://doi.org/10.1007/978-3-030-87231-1_44
Auteur(s) : Jérôme Schmid, Christophe Chênes
Haq, R., Schmid, J. , Borgie, R. , Cates, J. , Audette, M.
(2020). Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation : Journal of Medical Imaging.https://arodes.hes-so.ch/record/5374?ln=fr
Auteur(s) : Jérôme Schmid
Huang, R., Nedanoski, A., Fletcher, D. F., Singh, N., Schmid, J., Young, P. M. , Stow, N., Bi, L., Traini, D., Wong, E., Phillips, C. L., Grunstein, R. R. , Kin, J.
(2019). An automated segmentation framework for nasal computational fluid dynamics analysis in computed tomography : Computers in biology and medicine.https://arodes.hes-so.ch/record/4548?ln=fr
Auteur(s) : Jérôme Schmid
Damopoulos, D., Lerch, T. D., Schmaranzer, F., Tannast, M., Chênes, C., Zheng, G., Schmid, J.
(2019). Segmentation of the proximal femur in radial MR scans using a random forest classifier and deformable model registration : International Journal of Computer Assisted Radiology and Surgery.https://arodes.hes-so.ch/record/3965?ln=fr
Auteur(s) : Christophe Chênes, Jérôme Schmid
Audette, M. A., Schmid, J., Goodmurphy, C., Polanco, M., Bawab, S., Tapp, A., St-Clair, H. S.
(2018). Towards a deformable multi-surface approach to ligamentous spine models for predictive simulation-based scoliosis surgery planning : Proceedings of MICCAI Computational Methods and Clinical Applications for Spine Imaging, Granada, Spain, Lecture Notes for Computer Science (LNCS).https://doi.org/10.1007/978-3-030-13736-6_8
Auteur(s) : Jérôme Schmid
Charbonnier, C., Chague, S., Schmid, J., Kolo, F. C., Bernardoni, M., Christofilopoulos, P.
(2015). Analysis of hip range of motion in everyday life: a pilot study : Hip International.https://doi.org/10.5301/hipint.5000192
Auteur(s) : Jérôme Schmid
