Bulat Ibragimov
Lektor, Lektor - forfremmelsesprogrammet
Image Analysis, Computational Modelling and Geometry
Universitetsparken 1, 2100 København Ø
Medlem af:
ORCID: 0000-0001-7739-7788
1 - 4 ud af 4Pr. side: 10
- 2024
- Udgivet
Recognition of radiological decision errors from eye movement during chest X-ray readings
Anikina, Anna, Pershin, I., Mustafaev, T. & Ibragimov, Bulat, 2024, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment. Mello-Thoms, C. R., Mello-Thoms, C. R. & Chen, Y. (red.). SPIE, 4 s. 129290A. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Bind 12929).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › 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 › Forskning › fagfællebedømt
- E-pub ahead of print
Building an AI Support Tool for Real-Time Ulcerative Colitis Diagnosis
Møller, Bjørn Leth, Lo, B. Z. S., Burisch, J., Bendtsen, Flemming, Vind, Ida, Ibragimov, Bulat & Igel, Christian, 2024, (E-pub ahead of print) I: KI - Künstliche Intelligenz. 8 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
- 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 › 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
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Udgivet -
23
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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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