Dr. Yannick Suter

My main research area is the application of machine learning, deep learning, and radiomics to study Glioblastoma multiforme, a highly aggressive brain tumor. We leverage longitudinal Magnetic Resonance Imaging (MRI) data from the Inselspital and public data sources to explore patterns for treatment response assessment. Other topics include the robustness of radiomic features on MRI to changes in image acquisition parameters and multi-center studies, and the integration of non-imaging information and prior clinical knowledge.

Suter, Y., Knecht, U., Wiest, R., Hewer, E., Schucht, P., and Reyes, M., 2020. Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning. In Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology (pp. 129-138). Springer, Cham.

Suter, Y., Knecht, U., Wiest, R., and Reyes, M., 2020. Overall Survival Prediction for Glioblastoma on Pre-Treatment MRI Using Robust Radiomics and Priors. In International MICCAI Brainlesion Workshop, in press.

Suter, Y., Knecht, U., Alão, M., Valenzuela, W., Hewer, E., Schucht, P., Wiest, R., and Reyes, M., 2020. Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques. Cancer Imaging, 20(1), pp.1-13.

Rebsamen, M., Suter, Y., Wiest, R., Reyes, M., and Rummel, C., 2020. Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning. Frontiers in neurology, 11, p.244.

Bakas, S., Reyes, et int., Suter, Y., et int, and Menze, B., 2018. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. arXivpreprint arXiv:1811.02629.

Suter, Y., Jungo, A., Rebsamen, M., Knecht, U., Herrmann, E., Wiest, R., and Reyes, M., 2018. Deep learning versus classical regression for brain tumor patient survival prediction. In International MICCAI Brainlesion Workshop (pp. 429-440). Springer, Cham.

Suter, Y., Rummel, C., Wiest, R., and Reyes, M., 2018. Fast and uncertainty-aware cerebral cortex morphometry estimation using random forest regression. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 1052-1055). IEEE.

Zhang, F., Kahali, P., Suter, Y., Norton, I., Rigolo, L., Savadjiev, P., Song, Y., Rathi, Y., Cai, W., Wells, W.M., Golby, A.J., and O'Donnell, L.J., 2017. Automated connectivity-based GroupWise cortical atlas generation: Application to data of neurosurgical patients with brain tumors for cortical parcellation prediction. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)(pp. 774-777). IEEE.

O'Donnell, L.J., Suter, Y., Rigolo, L., Kahali, P., Zhang, F., Norton, I., Albi, A., Olubiyi, O., Meola, A., Essayed, W.I. and Unadkat, P., 2017. Automated white matter fiber tract identification in patients with brain tumors.NeuroImage: Clinical, 13, pp.138-153.

2014	Electrosuisse award for the BSc Thesis "Piezoelectric Energy Harvesting for HSRvote"

2017 - 2020	Supervision of MSc Theses and ERASMUS projects of MSc Students
2017 - 2020	Teaching Assistant for the Medical Image Analysis Laboratory course, MSc Program Biomedical Engineering

2017 - today	Ph.D. Student, Medical Image Analysis Group
2014 - 2017	MSc in Biomedical Engineering, University of Bern. Major in image-guided therapy. Master's thesis: Fast and Accurate Brain Cortex Modeling Using Machine Learning Techniques
2016 - 2016	Research Trainee, O'Donnell group, Laboratory for Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
2012 - 2014	BSc in Electrical Engineering, University of Applied Sciences Rapperswil