Bulat Ibragimov
Lektor, Lektor - forfremmelsesprogrammet
Image Analysis, Computational Modelling and Geometry
Universitetsparken 1, 2100 København Ã
Medlem af:
- E-pub ahead of print
vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images
Podobnik, G., Ibragimov, Bulat, Peterlin, P., Strojan, P. & Vrtovec, T., 2024, (E-pub ahead of print) I: Medical Physics. 12 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
- Udgivet
The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments - a systematic review
bzd222, bzd222, Dascalu, Tudor-Laurentiu, Bakhshandeh, Azam, Ibragimov, Bulat, Kvist, T., EndoReCo, E. & Bjørndal, Lars, 2023, I: Acta Odontologica Scandinavica. 81, 6, s. 422-435 14 s.Publikation: Bidrag til tidsskrift › Review › fagfællebedømt
- E-pub ahead of print
The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review
Ibragimov, Bulat & Mello-Thoms, C., 2024, (E-pub ahead of print) I: IEEE Journal of Biomedical and Health Informatics. 19 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
- Udgivet
Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation
Vrtovec, T. & Ibragimov, Bulat, 2022, I: European Spine Journal. 31, s. 2031–2045Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
- Udgivet
Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization
Yang, Q., Liu, X., Chen, Z., Ibragimov, Bulat & Yuan, Y., 2022, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings. Wang, L., Dou, Q., Fletcher, P. T., Speidel, S. & Li, S. (red.). Springer, s. 119-129 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 13438 LNCS).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck: Baseline Results
Podobnik, G., Ibragimov, Bulat, Strojan, P., Peterlin, P. & Vrtovec, T., 2022, 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, s. 1-4Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Prediction of pulp exposure before caries excavation using artificial intelligence: Deep learning-based image data versus standard dental radiographs
bzd222, bzd222, Dascalu, Tudor-Laurentiu, Ibragimov, Bulat, Bakhshandeh, Azam & Bjørndal, Lars, nov. 2023, I: Journal of Dentistry. 138, 7 s., 104732.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
- Udgivet
Physics-based loss and machine learning approach in application to non-Newtonian fluids flow modeling
Kornaeva, E., Kornaev, A., Fetisov, A., Stebakov, I. & Ibragimov, Bulat, 2022, 2022 IEEE Congress on Evolutionary Computation, CEC 2022 - Conference Proceedings. IEEE, 8 s.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Parotid gland segmentation with nnU-Net: deployment scenario and inter-observer variability analysis
Podobnik, G., Strojan, P., Peterlin, P., Ibragimov, Bulat & Vrtovec, T., 2022, Medical Imaging 2022: Image Processing. Colliot, O., Isgum, I., Landman, B. A. & Loew, M. H. (red.). SPIE, s. 1-8 120321N. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Bind 12032).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes
Ibragimov, Bulat, Toesca, D. A. S., Yuan, Y., Koong, A. C., Chang, D. T. & Xing, L., 2019, I: IEEE Journal of Biomedical and Health Informatics. 23, 5, s. 1821-1833 13 s., 8664101.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › fagfællebedømt
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Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
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Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification
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