Bulat Ibragimov
Associate Professor, Associate Professor - Promotion Programme
Image Analysis, Computational Modelling and Geometry
Universitetsparken 1, 2100 København Ø
Member of:
ORCID: 0000-0001-7739-7788
1 - 4 out of 4Page size: 500
- Published
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. (eds.). SPIE, 4 p. 129290A. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 12929).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
- 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) In: IEEE Journal of Biomedical and Health Informatics. 19 p.Research output: Contribution to journal › Journal article › Research › peer-review
- 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) In: KI - Künstliche Intelligenz. 8 p.Research output: Contribution to journal › Journal article › Research › peer-review
- 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) In: Medical Physics. 12 p.Research output: Contribution to journal › Journal article › Research › peer-review
ID: 219366603
Most downloads
-
241
downloads
Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients
Research output: Contribution to journal › Journal article › Research › peer-review
Published -
23
downloads
Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification
Research output: Contribution to journal › Journal article › Research › peer-review
Published