Bulat Ibragimov
Lektor, Lektor - forfremmelsesprogrammet
Image Analysis, Computational Modelling and Geometry
Universitetsparken 1, 2100 København Ø
Medlem af:
ORCID: 0000-0001-7739-7788
1 - 3 ud af 3Pr. side: 10
- 2023
- Udgivet
Assignment Theory-Augmented Neural Network for Dental Arch Labeling
Dascalu, Tudor-Laurentiu & Ibragimov, Bulat, 2023, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Greenspan, H., Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T. & Taylor, R. (red.). Springer, s. 295-304 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14222 LNCS).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data
Pershin, I., Mustafaev, T. & Ibragimov, Bulat, 2023, 2023 IEEE Congress on Evolutionary Computation, CEC 2023. IEEEPublikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
- Udgivet
Multimodal CT and MR Segmentation of Head and Neck Organs-at-Risk
Podobnik, G., Strojan, P., Peterlin, P., Ibragimov, Bulat & Vrtovec, T., 2023, Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings. Greenspan, H., Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T. & Taylor, R. (red.). Springer, s. 745-755 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 14223 LNCS).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
ID: 219366603
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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
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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