| 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 | 1779 | 2018 |
| 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 | 166 | 2018 |
| Ensembling neural networks for digital pathology images classification and segmentation A Pimkin, G Makarchuk, V Kondratenko, M Pisov, E Krivov, M Belyaev Image Analysis and Recognition: 15th International Conference, ICIAR 2018 …, 2018 | 35 | 2018 |
| Universal loss reweighting to balance lesion size inequality in 3D medical image segmentation B Shirokikh, A Shevtsov, A Kurmukov, A Dalechina, E Krivov, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 24 | 2020 |
| Dimensionality reduction with isomap algorithm for EEG covariance matrices E Krivov, M Belyaev 2016 4th International Winter Conference on Brain-Computer Interface (BCI), 1-4, 2016 | 17 | 2016 |
| Accelerating 3D medical image segmentation by adaptive small-scale target localization B Shirokikh, A Shevtsov, A Dalechina, E Krivov, V Kostjuchenko, ... Journal of Imaging 7 (2), 35, 2021 | 16 | 2021 |
| MRI augmentation via elastic registration for brain lesions segmentation E Krivov, M Pisov, M Belyaev Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2018 | 12 | 2018 |
| Tumor delineation for brain radiosurgery by a convnet and non-uniform patch generation E Krivov, V Kostjuchenko, A Dalechina, B Shirokikh, G Makarchuk, ... Patch-Based Techniques in Medical Imaging: 4th International Workshop, Patch …, 2018 | 9 | 2018 |
| Deep learning for brain tumor segmentation in radiosurgery: prospective clinical evaluation B Shirokikh, A Dalechina, A Shevtsov, E Krivov, V Kostjuchenko, ... International MICCAI Brainlesion Workshop, 119-128, 2019 | 7 | 2019 |
| The comparison of automatic artifact removal methods with robust classification strategies in terms of EEG classification accuracy P Merinov, M Belyaev, E Krivov 2015 International Conference on Biomedical Engineering and Computational …, 2015 | 5 | 2015 |
| Ensembling neural networks for digital pathology images classification and segmentation G Makarchuk, V Kondratenko, M Pisov, A Pimkin, E Krivov, M Belyaev arXiv preprint arXiv:1802.00947, 2018 | 4 | 2018 |
| Systematic clinical evaluation of a deep learning method for medical image segmentation: radiosurgery application B Shirokikh, A Dalechina, A Shevtsov, E Krivov, V Kostjuchenko, ... IEEE Journal of Biomedical and Health Informatics 26 (7), 3037-3046, 2022 | 3 | 2022 |
| Neural networks ensembles for ischemic stroke lesion segmentation M Pisov, M Belyaev, E Krivov | 3 | 2017 |
| Filter bank extension for neural network-based motor imagery classification P Merinov, M Belyaev, E Krivov 2016 IEEE 26th International Workshop on Machine Learning for Signal …, 2016 | 3 | 2016 |
| Using geometry of the set of symmetric positive semidefinite matrices to classify structural brain networks M Belyaev, Y Dodonova, D Belyaeva, E Krivov, B Gutman, J Faskowitz, ... Computational Aspects and Applications in Large-Scale Networks: NET 2017 …, 2018 | 1 | 2018 |
| Clinical evaluation of deep learning methods for brain tumor contouring A Dalechina, E Krivov, M Belyaev, V Kostjuchenko, B Shirokikh, ... EasyChair, 2019 | | 2019 |
| It doesn't take a whole U-Net to find brain tumors: towards fast brain metastasis segmentation with sparse predictions E Krivov, M Belyaev | | 2018 |
