Bulat Ibragimov
Lektor, Lektor - forfremmelsesprogrammet
Image Analysis, Computational Modelling and Geometry
Universitetsparken 1, 2100 København Ø
Medlem af:
- Udgivet
Densely Connected Neural Network with Unbalanced Discriminant and Category Sensitive Constraints for Polyp Recognition
Yuan, Y., Qin, W., Ibragimov, Bulat, Zhang, G., Han, B., Meng, M. Q. H. & Xing, L., 2020, I: IEEE Transactions on Automation Science and Engineering. 17, 2, s. 574-583 10 s., 8842597.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › 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
Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation
Yang, C., Guo, X., Zhu, M., Ibragimov, Bulat & Yuan, Y., 2021, I: IEEE Journal of Biomedical and Health Informatics. 25, 10, s. 3886-3897Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
- Udgivet
Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods
Vrtovec, T., Močnik, D., Strojan, P., Pernuš, F. & Ibragimov, Bulat, 2020, I: Medical Physics. 47, 9, s. e929-e950Publikation: Bidrag til tidsskrift › Review › Forskning › 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 › Forskning › fagfællebedømt
- Udgivet
Deep Learning for Diagnosis and Segmentation of Pneumothorax: The Results on The Kaggle Competition and Validation Against Radiologists
Tolkachev, A., Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T. & Ibragimov, Bulat, 2021, I: IEEE Journal of Biomedical and Health Informatics. 25, 5, s. 1660-1672Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
- Udgivet
Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography
Sirazitdinov, I., Kubrak, K., Kiselev, S., Tolkachev, A., Kholiavchenko, M. & Ibragimov, Bulat, 2020, Artificial Neural Networks and Machine Learning – ICANN 2020 - 29th International Conference on Artificial Neural Networks, Proceedings. Farkaš, I., Masulli, P. & Wermter, S. (red.). Springer VS, s. 247-257 11 s. (Lecture Notes in Computer Science, Bind 12396 LNCS).Publikation: 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 › Forskning › 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 › 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
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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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