| Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks B Ibragimov, L Xing Medical physics 44 (2), 547-557, 2017 | 576 | 2017 |
| A benchmark for comparison of dental radiography analysis algorithms CW Wang, CT Huang, JH Lee, CH Li, SW Chang, MJ Siao, TM Lai, ... Medical image analysis 31, 63-76, 2016 | 400 | 2016 |
| Fully automated quantitative cephalometry using convolutional neural networks SÖ Arık, B Ibragimov, L Xing Journal of Medical Imaging 4 (1), 014501-014501, 2017 | 279 | 2017 |
| Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database I Sirazitdinov, M Kholiavchenko, T Mustafaev, Y Yixuan, R Kuleev, ... Computers & electrical engineering 78, 388-399, 2019 | 243 | 2019 |
| Evaluation and comparison of anatomical landmark detection methods for cephalometric x-ray images: a grand challenge CW Wang, CT Huang, MC Hsieh, CH Li, SW Chang, WC Li, R Vandaele, ... IEEE transactions on medical imaging 34 (9), 1890-1900, 2015 | 215 | 2015 |
| A framework for automated spine and vertebrae interpolation-based detection and model-based segmentation R Korez, B Ibragimov, B Likar, F Pernuš, T Vrtovec IEEE transactions on medical imaging 34 (8), 1649-1662, 2015 | 154 | 2015 |
| A multi-center milestone study of clinical vertebral CT segmentation J Yao, JE Burns, D Forsberg, A Seitel, A Rasoulian, P Abolmaesumi, ... Computerized Medical Imaging and Graphics 49, 16-28, 2016 | 148 | 2016 |
| Auto‐segmentation of organs at risk for head and neck radiotherapy planning: from atlas‐based to deep learning methods T Vrtovec, D Močnik, P Strojan, F Pernuš, B Ibragimov Medical physics 47 (9), e929-e950, 2020 | 145 | 2020 |
| Prostate cancer classification with multiparametric MRI transfer learning model Y Yuan, W Qin, M Buyyounouski, B Ibragimov, S Hancock, B Han, L Xing Medical physics 46 (2), 756-765, 2019 | 139 | 2019 |
| Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT B Ibragimov, D Toesca, D Chang, Y Yuan, A Koong, L Xing Medical physics 45 (10), 4763-4774, 2018 | 138 | 2018 |
| Shape representation for efficient landmark-based segmentation in 3-D B Ibragimov, B Likar, F Pernuš, T Vrtovec IEEE transactions on medical imaging 33 (4), 861-874, 2014 | 134 | 2014 |
| Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation W Qin, J Wu, F Han, Y Yuan, W Zhao, B Ibragimov, J Gu, L Xing Physics in Medicine & Biology 63 (9), 095017, 2018 | 116 | 2018 |
| Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning B Ibragimov, D Toesca, D Chang, A Koong, L Xing Physics in Medicine & Biology 62 (23), 8943, 2017 | 103 | 2017 |
| Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients E Jimenez-Solem, TS Petersen, C Hansen, C Hansen, C Lioma, C Igel, ... Scientific reports 11 (1), 3246, 2021 | 90 | 2021 |
| Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge G Zheng, C Chu, DL Belavý, B Ibragimov, R Korez, T Vrtovec, H Hutt, ... Medical image analysis 35, 327-344, 2017 | 85 | 2017 |
| Learning deconvolutional deep neural network for high resolution medical image reconstruction H Liu, J Xu, Y Wu, Q Guo, B Ibragimov, L Xing Information Sciences 468, 142-154, 2018 | 81 | 2018 |
| A game-theoretic framework for landmark-based image segmentation B Ibragimov, B Likar, F Pernus IEEE Transactions on Medical Imaging 31 (9), 1761-1776, 2012 | 79 | 2012 |
| Automatic cephalometric X-ray landmark detection by applying game theory and random forests B Ibragimov, B Likar, F Pernus, T Vrtovec Proc. ISBI Int. Symp. on Biomedical Imaging, 1-8, 2014 | 60 | 2014 |
| Contour-aware multi-label chest X-ray organ segmentation M Kholiavchenko, I Sirazitdinov, K Kubrak, R Badrutdinova, R Kuleev, ... International Journal of Computer Assisted Radiology and Surgery 15, 425-436, 2020 | 58 | 2020 |
| Deep learning for diagnosis and segmentation of pneumothorax: The results on the Kaggle competition and validation against radiologists A Tolkachev, I Sirazitdinov, M Kholiavchenko, T Mustafaev, B Ibragimov IEEE Journal of Biomedical and Health Informatics 25 (5), 1660-1672, 2020 | 57 | 2020 |
Tomaž VrtovecUniversity of Ljubljana, Faculty of Electrical Engineeringfe.uni-lj.si üzerinde doğrulanmış e-posta adresine sahip
