| 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, ... Preprint, 2018 | 1993 | 2018 |
| The liver tumor segmentation benchmark (LiTS) P Bilic*, P Christ*, HB Li*, E Vorontsov, A Ben-Cohen, G Kaissis, ... Medical Image Analysis, 2022 | 1195 | 2022 |
| Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge VM Campello, P Gkontra, C Izquierdo, C Martin-Isla, A Sojoudi, PM Full, ... IEEE Transactions on Medical Imaging, 2021 | 349 | 2021 |
| 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, 2019 | 296 | 2019 |
| Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images H Li, G Jiang, J Zhang, R Wang, Z Wang, WS Zheng, B Menze NeuroImage, 2018 | 248 | 2018 |
| VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images A Sekuboyina, ME Husseini, A Bayat, M Löffler, H Liebl, H Li, G Tetteh, ... Medical Image Analysis, 2021 | 239 | 2021 |
| Knowledge-aided convolutional neural network for small organ segmentation Y Zhao, H Li, S Wan, A Sekuboyina, X Hu, G Tetteh, M Piraud, B Menze IEEE journal of biomedical and health informatics, 2019 | 199 | 2019 |
| 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, 2022 | 188 | 2022 |
| Automated whole-body bone lesion detection for multiple myeloma on 68Ga-pentixafor PET/CT imaging using deep learning methods L Xu, G Tetteh, J Lipkova, Y Zhao, H Li, P Christ, M Piraud, A Buck, K Shi, ... Contrast media & molecular imaging, 2018 | 154 | 2018 |
| DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis H Li, JC Paetzold, A Sekuboyina, F Kofler, J Zhang, JS Kirschke, ... MICCAI'2019, 2019 | 93 | 2019 |
| Deep learning-enabled multi-organ segmentation in whole-body mouse scans O Schoppe, C Pan, J Coronel, H Mai, Z Rong, MI Todorov, A Müskes, ... Nature Communications, 2020 | 81 | 2020 |
| Cardiac segmentation on late gadolinium enhancement MRI: a benchmark study from multi-sequence cardiac MR segmentation challenge X Zhuang, J Xu, X Luo, C Chen, C Ouyang, D Rueckert, VM Campello, ... Medical Image Analysis, 2022 | 70 | 2022 |
| Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: the ADAM challenge KM Timmins, IC van der Schaaf, E Bennink, YM Ruigrok, X An, ... Neuroimage 238, 118216, 2021 | 60 | 2021 |
| Relationformer:a unified framework for image-to-graph generation S Shit, R Koner, B Wittmann, J Paetzold, I Ezhov, H Li, J Pan, ... ECCV'2022, 2022 | 53 | 2022 |
| Red-GAN: attacking class imbalance via conditioned generation. Yet another medical imaging perspective AB Qasim, I Ezhov, S Shit, O Schoppe, JC Paetzold, A Sekuboyina, ... MIDL'2020, 2020 | 53 | 2020 |
| The brain tumor segmentation (BRATS) challenge 2023: Focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) AF Kazerooni, N Khalili, X Liu, D Haldar, Z Jiang, SM Anwar, J Albrecht, ... arXiv preprint arXiv:2305.17033, 2023 | 47 | 2023 |
| Deep-learning generated synthetic double inversion recovery images improve multiple sclerosis lesion detection T Finck*, H Li*, L Grundl, P Eichinger, M Bussas, M Mühlau, B Menze, ... Investigative Radiology, 2020 | 46 | 2020 |
| Coarse-to-fine adversarial networks and zone-based uncertainty analysis for NK/T-cell lymphoma segmentation in CT/PET images X Hu, R Guo, J Chen, H Li, D Waldmannstetter, Y Zhao, B Li, K Shi, ... IEEE journal of biomedical and health informatics, 2020 | 45 | 2020 |
| Cross-view relation networks for mammogram mass detection J Ma, S Liang, X Li, H Li, BH Menze, R Zhang, WS Zheng ICPR'2020, 2019 | 42 | 2019 |
| 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 | 41 | 2021 |
Bjoern H MenzeUniversität ZürichVerified email at uzh.ch
