| 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 | 1623 | 2018 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 802 | 2023 |
| Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks PF Christ, F Ettlinger, F Grün, MEA Elshaera, J Lipkova, S Schlecht, ... arXiv preprint arXiv:1702.05970, 2017 | 398 | 2017 |
| AI-based pathology predicts origins for cancers of unknown primary MY Lu, TY Chen, DFK Williamson, M Zhao, M Shady, J Lipkova, ... Nature 594 (7861), 106-110, 2021 | 331 | 2021 |
| 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, 2018 | 129 | 2018 |
| Personalized radiotherapy design for glioblastoma: Integrating mathematical tumor models, multimodal scans, and bayesian inference J Lipková, P Angelikopoulos, S Wu, E Alberts, B Wiestler, C Diehl, ... IEEE transactions on medical imaging 38 (8), 1875-1884, 2019 | 118 | 2019 |
| Federated learning for computational pathology on gigapixel whole slide images MY Lu, RJ Chen, D Kong, J Lipkova, R Singh, DFK Williamson, TY Chen, ... Medical image analysis 76, 102298, 2022 | 107 | 2022 |
| Identifying the best machine learning algorithms for brain tumor segmentation S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... progression assessment, and overall survival prediction in the BRATS …, 2018 | 105 | 2018 |
| Artificial intelligence for multimodal data integration in oncology J Lipkova, RJ Chen, B Chen, MY Lu, M Barbieri, D Shao, AJ Vaidya, ... Cancer cell 40 (10), 1095-1110, 2022 | 101 | 2022 |
| Pan-cancer integrative histology-genomic analysis via multimodal deep learning RJ Chen, MY Lu, DFK Williamson, TY Chen, J Lipkova, Z Noor, M Shaban, ... Cancer Cell 40 (8), 865-878. e6, 2022 | 97 | 2022 |
| Analysis of Brownian dynamics simulations of reversible bimolecular reactions J Lipková, KC Zygalakis, SJ Chapman, R Erban SIAM Journal on Applied Mathematics 71 (3), 714-730, 2011 | 85 | 2011 |
| Brats toolkit: translating brats brain tumor segmentation algorithms into clinical and scientific practice F Kofler, C Berger, D Waldmannstetter, J Lipkova, I Ezhov, G Tetteh, ... Frontiers in neuroscience, 125, 2020 | 70 | 2020 |
| Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies J Lipkova, TY Chen, MY Lu, RJ Chen, M Shady, M Williams, J Wang, ... Nature medicine 28 (3), 575-582, 2022 | 49 | 2022 |
| The Art of Theoretical Biology F Matthäus, S Matthäus, S Harris, T Hillen Springer, 2020 | 41 | 2020 |
| 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, ... Medical imaging with deep learning, 655-668, 2020 | 38 | 2020 |
| Algorithm fairness in ai for medicine and healthcare RJ Chen, TY Chen, J Lipkova, JJ Wang, DFK Williamson, MY Lu, S Sahai, ... arXiv preprint arXiv:2110.00603, 2021 | 36 | 2021 |
| CXCR4-targeted PET imaging of central nervous system B-cell lymphoma P Herhaus, J Lipkova, F Lammer, I Yakushev, T Vag, J Slotta-Huspenina, ... Journal of Nuclear Medicine 61 (12), 1765-1771, 2020 | 36 | 2020 |
| Predicting glioblastoma recurrence from preoperative MR scans using fractional-anisotropy maps with free-water suppression MC Metz, M Molina-Romero, J Lipkova, J Gempt, F Liesche-Starnecker, ... Cancers 12 (3), 728, 2020 | 29 | 2020 |
| Lits-liver tumor segmentation challenge P Christ, F Ettlinger, F Grün, J Lipkova, G Kaissis ISBI and MICCAI, 2017 | 23 | 2017 |
| Neural parameters estimation for brain tumor growth modeling I Ezhov, J Lipkova, S Shit, F Kofler, N Collomb, B Lemasson, E Barbier, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 22 | 2019 |
Bjoern H MenzeUniversität ZürichBestätigte E-Mail-Adresse bei uzh.ch
