Publications

2025

• T. Dascalu, S. Ramezanzade, A. Bakhshandeh, L. Bjørndal, R. Iurcov, T. Vrtovec, B. Ibragimov, DentAssignNet: Assignment network for dental cast labeling in the presence of dental abnormalities, IEEE Journal of Biomedical and Health Informatics, 2025
• B. Lo, B. Møller, C. Igel, S. Wildt, I. Vind, F. Bendtsen, J. Burisch, B. Ibragimov, Improving the real-time classification of disease severity in ulcerative colitis: Artificial intelligence as the trigger for a second opinion, The American Journal of Gastroenterology, 10.14309, 2025
• A. Anikina, D. Ibragimova, T. Mustafaev, C. Mello-Thoms, B. Ibragimov, Prediction of radiological decision errors from longitudinal analysis of gaze and image features, Artificial intelligence in medicine (160), 103051, 2025
• L. Khaertdinova, T. Shmykova, I. Pershin, A. Laryukov, A. Khanov, D. Zidikhanov, B. Ibragimov, Gaze assistance for efficient segmentation correction of medical images, IEEE Access, 2025
• B. L. Møller, S. Amiri, C. Igel, K. K. Wickstrøm, R. Jenssen, M. Keicher, M. F. Azampour, N. Navab, B. Ibragimov, NEMt: Fast Targeted Explanations for Medical Image Models via Neural Explanation Masks, Northern Lights Deep Learning Conference, 2025

2024

• A. Kornaev, D. Lvov, I. Pershin, S. Kiselev, D. Afonchikov, I. Bariev, B. Ibragimov, Automatic calculation of cardiometric coefficients on chest X-ray images, IEEE Access, 2024
• I. Stebakov, A. Kornaev, E. Kornaeva, N. Litvinenko, Y. Kazakov, O. Ivanov, B. Ibragimov, Artificial neural networks as a natural tool in solution of variational problems in hydrodynamics, IEEE Access, 2024
• S. Amiri, R. Karimzadeh, T. Vrtovec, E.G.S. Brandt, H. Thomsen, M. B. Andersen, C. F. Müller, A. B. Rodell, B. Ibragimov, Centerline-guided reinforcement learning model for pancreatic duct identifications, Journal of Medical Imaging, 064002, 2024
• E.G.S. Brandt, C. F. Müller, H. Thomsen, A. B. Rodell, B. Ibragimov, M. B. Andersen, Imaging the pancreas with photon-counting CT–A review of normal pancreatic anatomy, European Journal of Radiology, 111736, 2024
• G. Podobnik, B. Ibragimov, E. Tappeiner, C. Lee, J.S. Kim, Z. Mesbah, R. Modzelewski, Y. Ma, F. Yang, M. Rudecki, M. Wodziński, P. Peterlin, P. Strojan, T. Vrtovec, HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge, Radiotherapy and Oncology, 198, 110410, 2024
• S. Amiri, T. Vrtovec, T. Mustafaev, C.L. Deufel, H.S. Thomsen, M.H. Sillesen, E.G.S. Brandt, M.B. Andersen, C.F. Müller, B. Ibragimov, Reinforcement learning‐based anatomical maps for pancreas subregion and duct segmentation, Medical Physics, Early Access, 2024
• E.M.C. Huijben, et al., Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report, Medical image analysis, 103276, 2024
• T. Dascalu, S. Ramezanzade, A. Bakhshandeh, L. Bjørndal, B. Ibragimov, AI-initiated second opinions: a framework for advanced caries treatment planning, BMC Oral Health, 24 (1), 772, 2024
• B. Ibragimov, C. Mello-Thoms: The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review, IEEE Journal of Biomedical and Health Informatics, early access, 2024
• B.L. Møller, B.Z.S. Lo, J. Burisch, F. Bendtsen, I. Vind, B. Ibragimov, C. Igel: Building an AI Support Tool for Real-Time Ulcerative Colitis Diagnosis, KI-Künstliche Intelligenz, 1-8, 2024
• G. Podobnik, B. Ibragimov, P. Peterlin, P. Strojan, T. Vrtovec, vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images, Medical Physics, 2175-2186, 51 (3), 2024
• A. Anikina, R. Karimzadeh, D. Ibragimova, T. Mustafaev, C. Mello-Thoms, B. Ibragimov, Prediction of Radiological Diagnostic Errors from Eye Tracking Data Using Graph Neural Networks and Gaze-Guided Transformers, International Workshop on Graphs in Biomedical Image Analysis at MICCAI, 33-42, 2024
• A. Anikina, I. Pershin, T. Mustafaev, B. Ibragimov: Recognition of radiological decision errors from eye movement during chest x-ray readings, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 45-48, 2024
• D. Ocepek, G. Podobnik, B. Ibragimov, T. Vrtovec, Deep implicit statistical shape models for 3D lumbar vertebrae image delineation, Medical Imaging 2024: Image Processing 12926, 782-788, 2024

2023

• S. Ramezanzade, T.L. Dascalu, B. Ibragimov, A. Bakhshandeh, L. Bjørndal, Prediction of Pulp Exposure before Caries Excavation using Artificial Intelligence: Deep Learning-based Image Data versus Standard Dental Radiographs, Journal of Dentistry, 104732, 2023
• B. Ibragimov, J. Zhen, E. Ayvali, Deep learning for detection of clinical operations in robot-assisted percutaneous renal access, IEEE Access, 90358-90366, 11, 2023
• B. Ibragimov, K. Arzamasov, B. Maksudov, S. Kiselev, A. Mongolin, T. Mustafaev, D. Ibragimova, K. Evteeva, A. Andreychenko, S. Morozov, A 178-clinical-center experiment of integrating AI solutions for lung pathology diagnosis, Scientific Reports, 1135 (13), 2023
• I. Pershin, T. Mustafaev, D. Ibragimova, B. Ibragimov, Changes in Radiologists’ Gaze Patterns Against Lung X-rays with Different Abnormalities: A Randomized Experiment, Journal of Digital Imaging, 1-9, 2023
• G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, HaN-Seg: The head and neck organ‐at‐risk CT & MR segmentation dataset, Medical Physics, 1-11, 2023
• D. Alukaev, S. Kiselev, I. Pershin, B. Ibragimov, V. Ivanov, A. Kornaev, I. Titov: Cross-Modal Conceptualization in Bottleneck Models, Conference on Empirical Methods in Natural Language Processing, pages 5241–5253, 2023
• F. Yousefirizi, S. Amiri, B. Ibragimov, C. Gowdy, C. Uribe, A. Rahmim, Test-Time Augmentation Towards Improved Cross-Center PET/CT Segmentations by the Swin UNETR, 2023 IEEE Nuclear Science Symposium, Medical Imaging Conference and International Symposium on Room-Temperature Semiconductor Detectors (NSS MIC RTSD), 2023
• I. Pershin, T. Mustafaev, B. Ibragimov, Contrastive Learning Approach to Predict Radiologist’s Error Based on Gaze Data, 2023 IEEE Congress on Evolutionary Computation (CEC), 1-6, 2023
• G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, Multimodal CT and MR Segmentation of Head and Neck Organs-at-Risk, Medical Image Computing and Computer Assisted Intervention–MICCAI, 2023
• T. Dascalu, B. Ibragimov, Assignment-theory-augmented neural network for multi-object recognition, Medical Image Computing and Computer Assisted Intervention–MICCAI, 2023
• I. Pershin, T. Mustafaev, B. Ibragimov, Contrastive Learning Approach to Predict Radiologist’s Error Based on Gaze Data, 2023 IEEE Congress on Evolutionary Computation (CEC), 1-6, 2023

2022

• I. Pershin, B. Maksudov, T. Mustafaev, B. Ibragimov, AI-Based Extraction of Radiologists Gaze Patterns Corresponding to Lung Regions, International Conference on Intelligent Systems Design and Applications, 386-393, 2022
• I. Pershin, B. Maksudov, T. Mustafaev, B. Ibragimov, Check for updates AI-Based Extraction of Radiologists Gaze Patterns Corresponding to Lung Regions, Intelligent Systems Design and Applications: 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022), 716, 2022
• Q. Yang, X. Liu, Z. Chen, B. Ibragimov, Y. Yuan, Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization, Medical Image Computing and Computer Assisted Intervention–MICCAI, 2022
• S. Ramezanzade, T. Laurentiu, A. Bakhshandah, B. Ibragimov, T. Kvist, L. Bjørndal, The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments – a systematic review, Acta Odontologica Scandinavica, 1-14, 2022
• E Kornaeva, A Kornaev, A Fetisov, I Stebakov, B Ibragimov, Physics-based loss and machine learning approach in application to non-Newtonian fluids flow modeling, 2022 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2022
• I Pershin, M Kholiavchenko, B Maksudov, T Mustafaev, D Ibragimova, B Ibragimov, Artificial Intelligence for the Analysis of Workload-Related Changes in Radiologists’ Gaze Patterns, IEEE Journal of Biomedical and Health Informatics 26 (9), 4541-4550, 119-129, 2022
• D Alukaev, S Kiselev, T Mustafaev, A Ainur, B Ibragimov, T Vrtovec, A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation, European Spine Journal, 1-10, 2022
• I E I Bekkouch, B Maksudov, S Kiselev, T Mustafaev, T Vrtovec, B Ibragimov, Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification, Medical Image Analysis 78, 102417, 2022
• M Kholiavchenko, I Pershin, B Maksudov, T Mustafaev, Y Yuan, B Ibragimov, Gaze-based attention to improve the classification of lung diseases, Medical Imaging 2022: Image Processing, 77-80, 2022
• T L Dascalu, A Kuznetsov, B Ibragimov, Benefits of auxiliary information in deep learning-based teeth segmentation, Medical Imaging 2022: Image Processing, 805-813, 2022
• G Podobnik, P Strojan, P Peterlin, B Ibragimov, T Vrtovec, Parotid gland segmentation with nnU-Net: deployment scenario and inter-observer variability analysis, Medical Imaging 2022: Image Processing, 444-451, 2022
• I Pershin, M Kholiavchenko, B Maksudov, T Mustafaev, B Ibragimov, AI-based analysis of radiologist’s eye movements for fatigue estimation: a pilot study on chest X-rays, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 240-243, 2022
• G Podobnik, B Ibragimov, P Strojan, P Peterlin, T Vrtovec, Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck: Baseline Results, IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-4, 2022
• T Vrtovec, B Ibragimov, Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation, European Spine Journal, 1-15, 2022
• Q Yang, X Liu, Z Chen, B Ibragimov, Y Yuan, Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization, International Conference on Medical Image Computing and Computer-Assisted Intervention, 119-129, 2022

2021

• S Kiselev, B Maksudov, T Mustafaev, R Kuleev, B Ibragimov, Automating cardiothoracic ratio measurements in chest X-rays, 2021 International Conference” Nonlinearity, Information and Robotics, 1-4, 2021
• C. Yang, X. Guo, M. Zhu, B. Ibragimov, Y. Yuan, Mutual-prototype adaptation for cross-domain polyp segmentation, IEEE Journal of Biomedical and Health Informatics, 25 (10), 3886-3897, 2021
• I.E.I. Bekkouch, D.C. Nicolae, A. Khan, S.M.A. Kazmi, A.M. Khattak, B. Ibragimov, Adversarial reconstruction loss for domain generalization, IEEE Access 9, 42424-42437, 2021
• I.E.I. Bekkouch, T. Aidinovich, T. Vrtovec, R. Kuleev, B. Ibragimov, Multi-agent shape models for hip landmark detection in MR scans, Medical Imaging 2021: Image Processing 11596, 115960O, 2021
• E Jimenez-Solem, et al., Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients, Scientific reports 11 (1), 1-12, 2021

2020

• E. Kivotova, B. Maksudov, R. Kuleev, B. Ibragimov, Extracting clinical information from chest x-ray reports: A case study for Russian language, International Conference Nonlinearity, Information and Robotics (NIR), 1-6, 20202
• M. Kholiavchenko, B. Maksudov, I. Sirazitdinov, T. Mustafaev, R. Kuleev, B. Ibragimov, Adopting confident learning to eliminate uncertainty in chest X-ray images for lung nodules prediction, International Journal of Radiation Oncology, Biology, Physics 108 (3), e338, 2020
• B. Maksudov, S. Kiselev, M. Kholiavchenko, T. Mustafaev, R. Kuleev, B. Ibragimov, Automated localization of lung nodules from chest X-rays with deep neural networks, International Journal of Radiation Oncology, Biology, Physics 108 (3), e294, 2020
• Z. Chen, X. Guo, C. Yang, B. Ibragimov, Y. Yuan, Joint spatial-wavelet dual-stream network for super-resolution, International Conference on Medical Image Computing and Computer-Assisted Interventions, 184-193, 2020
• A. Tolkachev, I. Sirazitdinov, M. Kholiavchenko, T. Mustafaev, B. Ibragimov, Deep learning for diagnosis and segmentation of pneumothorax: the results on the Kaggle competition and validation against radiologists, IEEE Journal of Biomedical and Health Informatics 25 (5), 1660-1672, 2020
• I. Sirazitdinov, K. Kubrak, S. Kiselev, A. Tolkachev, M. Kholiavchenko, B. Ibragimov, Evaluation of deep learning methods for bone suppression from dual energy chest radiography, International Conference on Artificial Neural Networks, 247-257, 2020
• H. Liu, Y. Lin, B. Ibragimov, C. Zhang, Low dose 4D-CT super-resolution reconstruction via inter-plane motion estimation based on optical flow, Biomedical Signal Processing and Control 62, 102085, 2020
• T. Vrtovec, D. Močnik, P. Strojan, F. Pernuš, B. Ibragimov, Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods, Medical physics 47 (9), e929-e950, 2020
• B. Ibragimov, D.A.S. Toesca, D.T. Chang, Y. Yuan, A.C. Koong, L. Xing, I.R. Vogelius, Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy, Medical physics 47 (8), 3721-3731, 2020
• B. Ibragimov, D.A.S. Toesca, D.T. Chang, Y. Yuan, A.C. Koong, L Xing, Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning-based portal vein segmentation, Neurocomputing 392, 181-188, 2020
• M Kholiavchenko, I Sirazitdinov, K Kubrak, R Badrutdinova, R Kuleev, B. Ibragimov, Contour-aware multi-label chest X-ray organ segmentation, International journal of computer assisted radiology and surgery 15 (3), 425-436, 2020

2019

• Y. Yuan, W. Qin, B. Ibragimov, G. Zhang, B. Han, M.Q.H. Meng, L. Xing, Densely connected neural network with unbalanced discriminant and category sensitive constraints for polyp recognition, IEEE Transactions on Automation Science and Engineering 17 (2), 574-583, 2019
• I. Sirazitdinov, M. Kholiavchenko, T. Mustafaev, Y. Yixuan, R. Kuleev, B. Ibragimov, Deep neural network ensemble for pneumonia localization from a large-scale chest X-ray database, Computers & electrical engineering 78, 388-399, 2020
• I. Sirazitdinov, M. Kholiavchenko, R. Kuleev, B. Ibragimov, Data augmentation for chest pathologies classification, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI), 2019
• B. Ibragimov, D.A.S. Toesca, Y. Yuan, A.C. Koong, D.T. Chang, L. Xing, Neural networks for deep radiotherapy dose analysis and prediction of liver SBRT outcomes, IEEE journal of biomedical and health informatics 23 (5), 1821-1833, 2019
• Y. Yuan, W. Qin, M. Buyyounouski, B. Ibragimov, S. Hancock, B. Han, L Xing, Prostate cancer classification with multiparametric MRI transfer learning model, Medical physics 46 (2), 756-765, 2019

2018

• B. Ibragimov, D. Toesca, D.T. Chang, A.C. Koong, L. Xing, Predicting Survival after Liver SBRT by Deep Learning-Based Analysis of Treatment Dose Plans, International Journal of Radiation Oncology, Biology, Physics 102 (3), e56, 2018
• H. Liu, J. Xu, Y. Wu, Q. Guo, B. Ibragimov, L. Xing, Learning deconvolutional deep neural network for high resolution medical image reconstruction, Information Sciences 468, 142-154, 2018
• B, Ibragimov, D, Toesca, D, Chang, Y, Yuan, A, Koong, L, Xing, Development of deep neural network for individualized hepatobiliary toxicity prediction after liver SBRT, Medical physics 45 (10), 4763-4774, 2018
• B. Ibragimov, D.A.S. Toesca, Y. Yuan, A.C. Koong, D.T. Chang, L. Xing, Deep 3D dose analysis for prediction of outcomes after liver stereotactic body radiation therapy, International Conference on Medical Image Computing and Computer-Assisted Interventions, 2018
• Y. Yuan, W. Qin, B. Ibragimov, B. Han, L. Xing, RIIS-DenseNet: rotation-invariant and image similarity constrained densely connected convolutional network for polyp detection, International Conference on Medical Image Computing and Computer-Assisted Interventions, 2018
• D. Močnik, B. Ibragimov, L. Xing, P. Strojan, B. Likar, F. Pernuš, T. Vrtovec, Segmentation of parotid glands from registered CT and MR images, Physica Medica 52, 33-41, 2018
• B. Ibragimov, Y. Yuan, D.A.S. Toesca, D.T. Chang, A.C. Koong, L Xing, Convolutional Neural Networks for Identifying Correlation Between Dose Patterns Associated with Poor Survival and Early Local Recurrence After Metastatic Liver SBRT, Medical Physics 45 (6), E415-E415, 2018
• W. Qin, J. Wu, F. Han, Y. Yuan, W. Zhao, B. Ibragimov, J. Gu, L. Xing, Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation, Physics in Medicine & Biology 63 (9), 095017, 2018
• D.A.S. Toesca, B. Ibragimov, A.J. Koong, L. Xing, A.C. Koong, D.T. Chang, Strategies for prediction and mitigation of radiation-induced liver toxicity, Journal of radiation research 59 (suppl_1), i40-i49, 2018