| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 2314 | 2018 |
| ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI O Maier, BH Menze, J Von der Gablentz, L Häni, MP Heinrich, M Liebrand, ... Medical image analysis 35, 250-269, 2017 | 553 | 2017 |
| Standardized assessment of automatic segmentation of white matter hyperintensities and results of the WMH segmentation challenge HJ Kuijf, JM Biesbroek, J De Bresser, R Heinen, S Andermatt, M Bento, ... IEEE transactions on medical imaging 38 (11), 2556-2568, 2019 | 345 | 2019 |
| Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure O Commowick, A Istace, M Kain, B Laurent, F Leray, M Simon, SC Pop, ... Scientific reports 8 (1), 13650, 2018 | 295 | 2018 |
| Federated learning enables big data for rare cancer boundary detection S Pati, U Baid, B Edwards, M Sheller, SH Wang, GA Reina, P Foley, ... Nature communications 13 (1), 7346, 2022 | 247 | 2022 |
| Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation R McKinley, R Meier, R Wiest Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 217 | 2019 |
| ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI S Winzeck, A Hakim, R McKinley, JA Pinto, V Alves, C Silva, M Pisov, ... Frontiers in neurology 9, 679, 2018 | 179 | 2018 |
| Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation S Pereira, R Meier, R McKinley, R Wiest, V Alves, CA Silva, M Reyes Medical image analysis 44, 228-244, 2018 | 119 | 2018 |
| Relevance of the cerebral collateral circulation in ischaemic stroke: time is brain, but collaterals set the pace S Jung, R Wiest, J Gralla, R McKinley, HP Mattle, D Liebeskind Swiss medical weekly 147 (4950), w14538-w14538, 2017 | 114 | 2017 |
| Fully automated stroke tissue estimation using random forest classifiers (FASTER) R McKinley, L Häni, J Gralla, M El-Koussy, S Bauer, M Arnold, U Fischer, ... Journal of Cerebral Blood Flow & Metabolism 37 (8), 2728-2741, 2017 | 99 | 2017 |
| Triplanar ensemble of 3D-to-2D CNNs with label-uncertainty for brain tumor segmentation R McKinley, M Rebsamen, R Meier, R Wiest Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2020 | 89 | 2020 |
| Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks R McKinley, R Wepfer, F Aschwanden, L Grunder, R Muri, C Rummel, ... Scientific reports 11 (1), 1087, 2021 | 88 | 2021 |
| Stroke lesion outcome prediction based on MRI imaging combined with clinical information A Pinto, R Mckinley, V Alves, R Wiest, CA Silva, M Reyes Frontiers in neurology 9, 1060, 2018 | 87 | 2018 |
| Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence R McKinley, R Wepfer, L Grunder, F Aschwanden, T Fischer, C Friedli, ... NeuroImage: Clinical, 102104, 2019 | 77 | 2019 |
| Towards uncertainty-assisted brain tumor segmentation and survival prediction A Jungo, R McKinley, R Meier, U Knecht, L Vera, J Pérez-Beteta, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 73 | 2018 |
| Nabla-net: A deep dag-like convolutional architecture for biomedical image segmentation R McKinley, R Wepfer, T Gundersen, F Wagner, A Chan, R Wiest, ... Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2016 | 71 | 2016 |
| QU-BraTS: MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation-Analysis of Ranking Scores and Benchmarking Results R Mehta, A Filos, U Baid, C Sako, R McKinley, M Rebsamen, K Dätwyler, ... Journal of Machine Learning for Biomedical Imaging 1, 2022 | 54 | 2022 |
| Direct cortical thickness estimation using deep learning‐based anatomy segmentation and cortex parcellation M Rebsamen, C Rummel, M Reyes, R Wiest, R McKinley Human brain mapping 41 (17), 4804-4814, 2020 | 53 | 2020 |
| Automatic quality control in clinical 1H MRSI of brain cancer N Pedrosa de Barros, R McKinley, U Knecht, R Wiest, J Slotboom NMR in Biomedicine 29 (5), 563-575, 2016 | 50 | 2016 |
| Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient F Kofler, I Ezhov, F Isensee, F Balsiger, C Berger, M Koerner, B Demiray, ... arXiv preprint arXiv:2103.06205, 2021 | 47 | 2021 |
Roland WiestProfessor在 insel.ch 的电子邮件经过验证
