| An adaptive intelligence algorithm for undersampled knee MRI reconstruction N Pezzotti, S Yousefi, MS Elmahdy, JHF Van Gemert, C Schuelke, ... IEEE Access 8, 204825-204838, 2020 | 117 | 2020 |
| Image quality assessment for magnetic resonance imaging S Kastryulin, J Zakirov, N Pezzotti, DV Dylov IEEE Access 11, 14154-14168, 2023 | 42 | 2023 |
| PyTorch Image Quality: Metrics and measure for image quality assessment S Kastryulin, D Zakirov, D Prokopenko Open-source software available at https://github. com/photosynthesisteam/piq, 2019 | 33* | 2019 |
| Pytorch image quality: Metrics for image quality assessment S Kastryulin, J Zakirov, D Prokopenko, DV Dylov arXiv preprint arXiv:2208.14818, 2022 | 30 | 2022 |
| Adaptive-CS-Net: FastMRI with adaptive intelligence N Pezzotti, E de Weerdt, S Yousefi, MS Elmahdy, J van Gemert, ... arXiv preprint arXiv:1912.12259, 2019 | 20 | 2019 |
| Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge PM Johnson, G Jeong, K Hammernik, J Schlemper, C Qin, J Duan, ... Machine Learning for Medical Image Reconstruction: 4th International …, 2021 | 17 | 2021 |
| An adaptive intelligence algorithm for undersampled knee mri reconstruction: Application to the 2019 fastmri challenge N Pezzotti, S Yousefi, MS Elmahdy, J Van Gemert, C Schülke, M Doneva, ... arXiv preprint arXiv:2004.07339, 2020 | 17 | 2020 |
| Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution A Belov, J Stadelmann, S Kastryulin, DV Dylov Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 11 | 2021 |
| Adaptive-CS-Net: FastMRI with Adaptive Intelligence. arXiv 2019 N Pezzotti, E de Weerdt, S Yousefi, MS Elmahdy, J van Gemert, ... arXiv preprint arXiv:1912.12259, 0 | 9 | |
| Pytorch image quality: Metrics for image quality assessment, 2022 S Kastryulin, J Zakirov, D Prokopenko, DV Dylov URL: https://arxiv. org/abs/2208.14818, doi 10, 2022 | 5 | 2022 |
| Does Diffusion Beat GAN in Image Super Resolution? D Kuznedelev, V Startsev, D Shlenskii, S Kastryulin arXiv preprint arXiv:2405.17261, 2024 | 2 | 2024 |
| Digital reference objects for evaluating algorithm performance in MR image formation C Wülker, NT Gessert, M Doneva, S Kastryulin, E Ercan, T Nielsen Magnetic Resonance Imaging 105, 67-74, 2024 | 1 | 2024 |
| QUASAR: QUality and Aesthetics Scoring with Advanced Representations S Kastryulin, D Prokopenko, A Babenko, DV Dylov IEEE Access, 2024 | | 2024 |
| Saliency maps for medical imaging S Kastryulin, A Chernyavskiy, N Pezzotti US Patent App. 18/683,595, 2024 | | 2024 |
| Out of distribution testing for magnetic resonance imaging S Kastryulin, A Tsanda, N Pezzotti US Patent App. 18/570,672, 2024 | | 2024 |
| YaART: Yet Another ART Rendering Technology S Kastryulin, A Konev, A Shishenya, E Lyapustin, A Khurshudov, ... arXiv preprint arXiv:2404.05666, 2024 | | 2024 |
| Sequential out of distribution detection for medical imaging N Pezzotti, C Wuelker, T Nielsen, K Sommer, M Grass, H Schulz, ... US Patent App. 18/031,889, 2023 | | 2023 |
| Unsupervised generation of artistic representations R Steinberg, S Kastryulin Twelfth International Conference on Machine Vision (ICMV 2019) 11433, 767-776, 2020 | | 2020 |
Nicola PezzottiDirector of Data Science, Philips Cardiologs - Assistant Professor, TU/eVerified email at philips.com
