| CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation AE Kavur, NS Gezer, M Barış, S Aslan, PH Conze, V Groza, DD Pham, ... Medical Image Analysis 69, 101950, 2021 | 458 | 2021 |
| Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging M Perkonigg, J Hofmanninger, CJ Herold, JA Brink, O Pianykh, H Prosch, ... Nature communications 12 (1), 5678, 2021 | 47 | 2021 |
| Dynamic memory to alleviate catastrophic forgetting in continuous learning settings J Hofmanninger, M Perkonigg, JA Brink, O Pianykh, C Herold, G Langs Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 21 | 2020 |
| Continual active learning for efficient adaptation of machine learning models to changing image acquisition M Perkonigg, J Hofmanninger, G Langs International Conference on Information Processing in Medical Imaging, 649-660, 2021 | 17 | 2021 |
| Debdoot Sheet, Gurbandurdy Dovletov, Oliver Speck, Andreas Nürnberger, Klaus H. Maier-Hein, Gözde Bozdagı Akar, Gözde Unal, Oguz Dicle, and M. Alper Selver. CHAOS Challenge … AE Kavur, NS Gezer, M Barıs, S Aslan, PH Conze, V Groza, DD Pham, ... Medical Image Analysis 69 (101950), 16, 2021 | 16 | 2021 |
| Detecting bone lesions in multiple myeloma patients using transfer learning M Perkonigg, J Hofmanninger, B Menze, MA Weber, G Langs Data Driven Treatment Response Assessment and Preterm, Perinatal, and …, 2018 | 6 | 2018 |
| Unsupervised deep clustering for predictive texture pattern discovery in medical images M Perkonigg, D Sobotka, A Ba-Ssalamah, G Langs arXiv preprint arXiv:2002.03721, 2020 | 5 | 2020 |
| Maschinelles Lernen in der Radiologie: Begriffsbestimmung vom Einzelzeitpunkt bis zur Trajektorie. G Langs, U Attenberger, R Licandro, J Hofmanninger, M Perkonigg, ... Der Radiologe 60 (1), 2020 | 5 | 2020 |
| Asymmetric cascade networks for focal Bone lesion prediction in multiple myeloma R Licandro, J Hofmanninger, M Perkonigg, S Röhrich, MA Weber, ... arXiv preprint arXiv:1907.13539, 2019 | 5 | 2019 |
| Continual active learning using pseudo-domains for limited labelling resources and changing acquisition characteristics M Perkonigg, J Hofmanninger, C Herold, H Prosch, G Langs arXiv preprint arXiv:2111.13069, 2021 | 4 | 2021 |
| Machine learning in radiology: terminology from individual timepoint to trajectory G Langs, U Attenberger, R Licandro, J Hofmanninger, M Perkonigg, ... Der Radiologe 60, 6-14, 2020 | 2 | 2020 |
| Correlation of histologic, imaging, and artificial intelligence features in NAFLD patients, derived from Gd-EOB-DTPA-enhanced MRI: a proof-of-concept study N Bastati, M Perkonigg, D Sobotka, S Poetter-Lang, R Fragner, A Beer, ... European Radiology 33 (11), 7729-7743, 2023 | 1 | 2023 |
| Identifying Phenotypic Concepts Discriminating Molecular Breast Cancer Sub-Types C Fürböck, M Perkonigg, T Helbich, K Pinker, V Romeo, G Langs International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 1 | 2022 |
| Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training D Sobotka, A Herold, M Perkonigg, L Beer, N Bastati, A Sablatnig, ... Computerized Medical Imaging and Graphics 114, 102369, 2024 | | 2024 |
| Pseudo-domains in imaging data improve prediction of future disease status in multi-center studies M Perkonigg, P Mesenbrink, A Goehler, M Martic, A Ba-Ssalamah, ... arXiv preprint arXiv:2111.07634, 2021 | | 2021 |
| Spatio Temporal Risk Prediction of Focal Bone Lesion Evolution in Multiple Myeloma R Licandro, M Perkonigg, S Röhrich, MA Weber, M Wennmann, L Kintzele, ... | | 2021 |
| Evolution Risk Prediction of Bone Lesions in Multiple Myeloma R Licandro, J Hofmanninger, M Perkonigg, S Röhrich, MA Weber, ... | | 2020 |
| Convolutional neural networks for bone lesion detection in medical imaging data M Perkonigg Wien, 2018 | | 2018 |
Georg LangsMedical University of Vienna, CIR LabGeverifieerd e-mailadres voor meduniwien.ac.at
