Suter, Yannick Raphael

Up a level
Export as [feed] RSS
Group by: Date | Item Type | Refereed | No Grouping
Jump to: 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018

2024

Pahud de Mortanges, Aurélie; Luo, Haozhe; Shu, Shelley Zixin; Kamath, Amith; Suter, Yannick; Shelan, Mohamed; Pöllinger, Alexander; Reyes, Mauricio (2024). Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging. NPJ digital medicine, 7(1) Nature Publishing Group 10.1038/s41746-024-01190-w

2023

Zbinden, Lukas; Catucci, Damiano; Suter, Yannick Raphael; Hulbert, Leona; Berzigotti, Annalisa; Brönnimann, Michael; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas (2023). Automated liver segmental volume ratio quantification on non-contrast T1-Vibe Dixon liver MRI using deep learning. European journal of radiology, 167, p. 111047. Elsevier 10.1016/j.ejrad.2023.111047

Ocaña-Tienda, Beatriz; Pérez-Beteta, Julián; Villanueva-García, José D; Romero-Rosales, José A; Molina-García, David; Suter, Yannick; Asenjo, Beatriz; Albillo, David; Ortiz de Mendivil, Ana; Pérez-Romasanta, Luis A; González-Del Portillo, Elisabet; Llorente, Manuel; Carballo, Natalia; Nagib-Raya, Fátima; Vidal-Denis, Maria; Luque, Belén; Reyes, Mauricio; Arana, Estanislao; Pérez-García, Víctor M (2023). A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data. Scientific data, 10(1), p. 208. 10.1038/s41597-023-02123-0

Rüfenacht, Elias; Kamath, Amith; Suter, Yannick; Poel, Robert; Ermiş, Ekin; Scheib, Stefan; Reyes, Mauricio (2023). PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion. Computer methods and programs in biomedicine, 231(107374), p. 107374. Elsevier 10.1016/j.cmpb.2023.107374

Suter, Yannick; Notter, Michelle; Meier, Raphael; Loosli, Tina; Schucht, Philippe; Wiest, Roland; Reyes, Mauricio; Knecht, Urspeter (2023). Evaluating automated longitudinal tumor measurements for glioblastoma response assessment. Frontiers in radiology, 3(1211859), p. 1211859. Frontiers Media 10.3389/fradi.2023.1211859

2022

Zbinden, Lukas; Catucci, Damiano; Suter, Yannick; Berzigotti, Annalisa; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas (2022). Convolutional neural network for automated segmentation of the liver and its vessels on non-contrast T1 vibe Dixon acquisitions. Scientific Reports, 12(1) Nature Publishing Group 10.1038/s41598-022-26328-2

Suter, Yannick; Knecht, Urspeter; Valenzuela, Waldo; Notter, Michelle; Hewer, Ekkehard; Schucht, Philippe; Wiest, Roland; Reyes, Mauricio (2022). The LUMIERE dataset: Longitudinal Glioblastoma MRI with expert RANO evaluation. Scientific data, 9(1), p. 768. Nature Publishing Group 10.1038/s41597-022-01881-7

Zbinden, Lukas; Catucci, D; Suter, Yannick Raphael; Berzigotti, Annalisa; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas (July 2022). Effectiveness of a state-of-the art neural network for liver parenchyma, portal and hepatic vein segmentation based on a standard non-contrast T1-vibe Dixon sequence (Unpublished). In: ECR 2022.

Zbinden, Lukas; Catucci, D; Suter, Yannick Raphael; Berzigotti, Annalisa; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas (July 2022). Automated liver segmental volume ratio (LSVR) quantification on non-contrast T1 vibe Dixon liver MRI using an artificial neural network (Unpublished). In: ECR 2022.

Zbinden, Lukas; Catucci, D; Suter, Yannick Raphael; Berzigotti, Annalisa; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas (May 2022). Effectiveness of a state-of-the-art neural network for liver parenchyma, portal and hepatic vein segmentation based on a standard non-contrast T1-vibe Dixon sequence (Unpublished). In: ESGAR 2022. Lisbon. May 2022.

Zbinden, Lukas; Catucci, D; Suter, Yannick Raphael; Berzigotti, Annalisa; Ebner, Lukas; Christe, Andreas; Obmann, Verena Carola; Sznitman, Raphael; Huber, Adrian Thomas (May 2022). Automated liver segmental volume ratio (LSVR) quantification on non-contrast T1 vibe dixon liver MRI using an artificial neural network (Unpublished). In: ESGAR 2022. Lisbon. May 2022.

2021

Suter, Yannick Raphael (2021). Advanced Machine Learning Technologies for Robust Longitudinal Radiomics and Response Assessment in Glioblastoma Multiforme. (Dissertation, Universität Bern, Medizinische Fakultät)

Suter, Yannick; Knecht, Urspeter; Wiest, Roland; Reyes, Mauricio (2021). Overall Survival Prediction for Glioblastoma on Pre-treatment MRI Using Robust Radiomics and Priors. Lecture notes in computer science, 12658, pp. 307-317. Springer 10.1007/978-3-030-72084-1_28

2020

Suter, Yannick; Knecht, Urspeter; Alao, Mariana; Valenzuela, Waldo; Hewer, Ekkehard; Schucht, Philippe; Wiest, Roland; Reyes, Mauricio (2020). Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques. Cancer imaging : the official publication of the International Cancer Imaging Society, 20(55), pp. 1-13. BioMed Central 10.1186/s40644-020-00329-8

Rebsamen, Michael; Suter, Yannick; Wiest, Roland; Reyes, Mauricio; Rummel, Christian (2020). Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning. Frontiers in neurology, 11(244), p. 244. Frontiers Media S.A. 10.3389/fneur.2020.00244

2019

Suter, Yannick Raphael; Jungo, Alain; Rebsamen, Michael; Knecht, Urspeter; Herrmann, Evelyn; Wiest, Roland; Reyes, Mauricio (26 January 2019). Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction. Lecture notes in computer science, 11384, pp. 429-440. Cham: Springer 10.1007/978-3-030-11726-9_38

2018

Suter, Yannick Raphael; Rummel, Christian; Wiest, Roland; Reyes, Mauricio (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 10.1109/ISBI.2018.8363752

Provide Feedback