| Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer BE Bejnordi, M Veta, PJ Van Diest, B Van Ginneken, N Karssemeijer, ... Jama 318 (22), 2199-2210, 2017 | 2896 | 2017 |
| Breast cancer histopathology image analysis: A review M Veta, JPW Pluim, PJ Van Diest, MA Viergever IEEE transactions on biomedical engineering 61 (5), 1400-1411, 2014 | 739 | 2014 |
| Assessment of algorithms for mitosis detection in breast cancer histopathology images M Veta, PJ Van Diest, SM Willems, H Wang, A Madabhushi, A Cruz-Roa, ... Medical image analysis 20 (1), 237-248, 2015 | 500 | 2015 |
| Automatic nuclei segmentation in H&E stained breast cancer histopathology images M Veta, PJ Van Diest, R Kornegoor, A Huisman, MA Viergever, ... PloS one 8 (7), e70221, 2013 | 444 | 2013 |
| Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge M Veta, YJ Heng, N Stathonikos, BE Bejnordi, F Beca, T Wollmann, ... Medical image analysis 54, 111-121, 2019 | 283 | 2019 |
| 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 40 (12), 3543-3554, 2021 | 278 | 2021 |
| A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging Z Xiong, Q Xia, Z Hu, N Huang, C Bian, Y Zheng, S Vesal, N Ravikumar, ... Medical image analysis 67, 101832, 2021 | 229 | 2021 |
| Going fully digital: Perspective of a Dutch academic pathology lab N Stathonikos, M Veta, A Huisman, PJ van Diest Journal of Pathology Informatics, 2013 | 196 | 2013 |
| Roto-translation covariant convolutional networks for medical image analysis EJ Bekkers, MW Lafarge, M Veta, KAJ Eppenhof, JPW Pluim, R Duits Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 178 | 2018 |
| Adversarial training and dilated convolutions for brain MRI segmentation P Moeskops, M Veta, MW Lafarge, KAJ Eppenhof, JPW Pluim Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 168 | 2017 |
| Domain-adversarial neural networks to address the appearance variability of histopathology images MW Lafarge, JPW Pluim, KAJ Eppenhof, P Moeskops, M Veta Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 135 | 2017 |
| Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images M Veta, A Huisman, MA Viergever, PJ van Diest, JPW Pluim 2011 IEEE international symposium on biomedical imaging: from nano to macro …, 2011 | 130 | 2011 |
| Deep learning regression for prostate cancer detection and grading in bi-parametric MRI C De Vente, P Vos, M Hosseinzadeh, J Pluim, M Veta IEEE Transactions on Biomedical Engineering 68 (2), 374-383, 2020 | 125 | 2020 |
| Deformable image registration using convolutional neural networks KAJ Eppenhof, MW Lafarge, P Moeskops, M Veta, JPW Pluim Medical Imaging 2018: Image Processing 10574, 192-197, 2018 | 117 | 2018 |
| Mitosis counting in breast cancer: Object-level interobserver agreement and comparison to an automatic method M Veta, PJ Van Diest, M Jiwa, S Al-Janabi, JPW Pluim PloS one 11 (8), e0161286, 2016 | 114 | 2016 |
| Prognostic value of automatically extracted nuclear morphometric features in whole slide images of male breast cancer M Veta, R Kornegoor, A Huisman, AHJ Verschuur-Maes, MA Viergever, ... Modern pathology 25 (12), 1559-1565, 2012 | 89 | 2012 |
| Mitosis domain generalization in histopathology images—the MIDOG challenge M Aubreville, N Stathonikos, CA Bertram, R Klopfleisch, N Ter Hoeve, ... Medical Image Analysis 84, 102699, 2023 | 84 | 2023 |
| Adversarial attack vulnerability of medical image analysis systems: Unexplored factors G Bortsova, C González-Gonzalo, SC Wetstein, F Dubost, I Katramados, ... Medical Image Analysis 73, 102141, 2021 | 79 | 2021 |
| Roto-translation equivariant convolutional networks: Application to histopathology image analysis MW Lafarge, EJ Bekkers, JPW Pluim, R Duits, M Veta Medical Image Analysis 68, 101849, 2021 | 75 | 2021 |
| Deep‐learning‐based preprocessing for quantitative myocardial perfusion MRI CM Scannell, M Veta, ADM Villa, EC Sammut, J Lee, M Breeuwer, ... Journal of Magnetic Resonance Imaging 51 (6), 1689-1696, 2020 | 70 | 2020 |
