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Bulat Ibragimov
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2020 – today
- 2024
- [j26]Ivan Stebakov
, Alexei Kornaev, Elena Kornaeva
Artificial Neural Networks as a Natural Tool in Solution of Variational Problems in Hydrodynamics. IEEE Access 12: 169945-169954 (2024) - [j25]Evi M. C. Huijben
Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report. Medical Image Anal. 97: 103276 (2024) - [j24]Bulat Ibragimov
The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review. IEEE J. Biomed. Health Informatics 28(6): 3597-3612 (2024) - [c30]Bjørn Leth Møller, Christian Igel, Kristoffer Knutsen Wickstrøm, Jon Sporring, Robert Jenssen, Bulat Ibragimov:
Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks. ICML 2024 - [c29]Bulat Ibragimov
Learn Together Stop Apart: An Inclusive Approach to Ensemble Pruning. KDD 2024: 1166-1176 - [c28]Bulat Ibragimov
Uplift Modelling via Gradient Boosting. KDD 2024: 1177-1187 - [i7]Leila Khaertdinova, Ilya Pershin, Tatiana Shmykova, Bulat Ibragimov:
Gaze-Assisted Medical Image Segmentation. CoRR abs/2410.17920 (2024) - 2023
- [j23]Bulat Ibragimov
Deep Learning for Detection of Clinical Operations in Robot-Assisted Percutaneous Renal Access. IEEE Access 11: 90358-90366 (2023) - [j22]Ilya Pershin, Tamerlan Mustafaev, Dilyara Ibragimova, Bulat Ibragimov
Changes in Radiologists' Gaze Patterns Against Lung X-rays with Different Abnormalities: a Randomized Experiment. J. Digit. Imaging 36(3): 767-775 (2023) - [c27]Ilya Pershin, Tamerlan Mustafaev, Bulat Ibragimov
Contrastive Learning Approach to Predict Radiologist's Error Based on Gaze Data. CEC 2023: 1-6 - [c26]Danis Alukaev, Semen Kiselev, Ilya Pershin, Bulat Ibragimov, Vladimir Ivanov, Alexey Kornaev, Ivan Titov:
Cross-Modal Conceptualization in Bottleneck Models. EMNLP 2023: 5241-5253 - [c25]Tudor Dascalu, Bulat Ibragimov
Assignment Theory-Augmented Neural Network for Dental Arch Labeling. MICCAI (3) 2023: 295-304 - [c24]Gasper Podobnik
Multimodal CT and MR Segmentation of Head and Neck Organs-at-Risk. MICCAI (4) 2023: 745-755 - [i6]Achraf Ben-Hamadou, Oussama Smaoui, Ahmed Rekik, Sergi Pujades, Edmond Boyer, Hoyeon Lim, Minchang Kim, Minkyung Lee, Minyoung Chung, Yeong-Gil Shin, Mathieu Leclercq, Lucia H. S. Cevidanes, Juan Carlos Prieto, Shaojie Zhuang, Guangshun Wei, Zhiming Cui, Yuanfeng Zhou, Tudor Dascalu, Bulat Ibragimov, Tae-Hoon Yong, Hong-Gi Ahn, Wan Kim, Jae-Hwan Han, Byungsun Choi, Niels van Nistelrooij, Steven Kempers, Shankeeth Vinayahalingam, Julien Strippoli, Aurélien Thollot, Hugo Setbon, Cyril Trosset, Edouard Ladroit:
3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge. CoRR abs/2305.18277 (2023) - [i5]Tudor Dascalu, Shaqayeq Ramezanzade, Azam Bakhshandeh, Lars Bjorndal, Bulat Ibragimov:
A Sequential Framework for Detection and Classification of Abnormal Teeth in Panoramic X-rays. CoRR abs/2309.00027 (2023) - [i4]Danis Alukaev
Cross-Modal Conceptualization in Bottleneck Models. CoRR abs/2310.14805 (2023) - 2022
- [j21]Imad Eddine Ibrahim Bekkouch
Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification. Medical Image Anal. 78: 102417 (2022) - [j20]Ilya Pershin
Artificial Intelligence for the Analysis of Workload-Related Changes in Radiologists' Gaze Patterns. IEEE J. Biomed. Health Informatics 26(9): 4541-4550 (2022) - [c23]Aleksandr Nesterov
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization. AIME 2022: 76-87 - [c22]Elena Kornaeva
Physics-based loss and machine learning approach in application to non-Newtonian fluids flow modeling. CEC 2022: 1-8 - [c21]Gasper Podobnik
Segmentation of Organs-At-Risk from Ct and Mr Images of the Head and Neck: Baseline Results. ISBI 2022: 1-4 - [c20]Ilya Pershin, Bulat Maksudov, Tamerlan Mustafaev, Bulat Ibragimov:
AI-Based Extraction of Radiologists Gaze Patterns Corresponding to Lung Regions. ISDA (3) 2022: 386-393 - [c19]Qiushi Yang, Xinyu Liu
Semi-supervised Medical Image Classification with Temporal Knowledge-Aware Regularization. MICCAI (8) 2022: 119-129 - [c18]Tudor Dascalu
Benefits of auxiliary information in deep learning-based teeth segmentation. Image Processing 2022 - [c17]Maksim Kholiavchenko, Ilya Pershin
Gaze-based attention to improve the classification of lung diseases. Image Processing 2022 - [c16]Gasper Podobnik
Parotid gland segmentation with nnU-Net: deployment scenario and inter-observer variability analysis. Image Processing 2022 - [c15]Ilya Pershin
AI-based analysis of radiologist's eye movements for fatigue estimation: a pilot study on chest X-rays. Image Perception, Observer Performance, and Technology Assessment 2022 - [i3]Aleksandr Nesterov, Bulat Ibragimov, Dmitriy Umerenkov, Artem Shelmanov, Galina Zubkova, Vladimir Kokh:
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic Regularization. CoRR abs/2206.00906 (2022) - [i2]Sepideh Amiri, Bulat Ibragimov:
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time Augmentation. CoRR abs/2210.07761 (2022) - 2021
- [j19]Imad Eddine Ibrahim Bekkouch
Adversarial Reconstruction Loss for Domain Generalization. IEEE Access 9: 42424-42437 (2021) - [j18]Alexey Tolkachev
Deep Learning for Diagnosis and Segmentation of Pneumothorax: The Results on the Kaggle Competition and Validation Against Radiologists. IEEE J. Biomed. Health Informatics 25(5): 1660-1672 (2021) - [j17]Chen Yang
Mutual-Prototype Adaptation for Cross-Domain Polyp Segmentation. IEEE J. Biomed. Health Informatics 25(10): 3886-3897 (2021) - [c14]Imad Eddine Ibrahim Bekkouch
Multi-agent shape models for hip landmark detection in MR scans. Image Processing 2021 - 2020
- [j16]Hui Liu, Yuxiu Lin, Bulat Ibragimov
Low dose 4D-CT super-resolution reconstruction via inter-plane motion estimation based on optical flow. Biomed. Signal Process. Control. 62: 102085 (2020) - [j15]Maksym Kholiavchenko
Contour-aware multi-label chest X-ray organ segmentation. Int. J. Comput. Assist. Radiol. Surg. 15(3): 425-436 (2020) - [j14]Bulat Ibragimov
Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning-based portal vein segmentation. Neurocomputing 392: 181-188 (2020) - [j13]Yixuan Yuan
Densely Connected Neural Network With Unbalanced Discriminant and Category Sensitive Constraints for Polyp Recognition. IEEE Trans Autom. Sci. Eng. 17(2): 574-583 (2020) - [c13]Ilyas Sirazitdinov, Konstantin Kubrak, Semen Kiselev, Alexey Tolkachev, Maksym Kholiavchenko
Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography. ICANN (1) 2020: 247-257 - [c12]Zhen Chen
Joint Spatial-Wavelet Dual-Stream Network for Super-Resolution. MICCAI (5) 2020: 184-193
2010 – 2019
- 2019
- [j12]Ilyas Sirazitdinov, Maksym Kholiavchenko
Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database. Comput. Electr. Eng. 78: 388-399 (2019) - [j11]Bulat Ibragimov
Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes. IEEE J. Biomed. Health Informatics 23(5): 1821-1833 (2019) - [c11]Ilyas Sirazitdinov, Maksym Kholiavchenko
Data Augmentation for Chest Pathologies Classification. ISBI 2019: 1216-1219 - [c10]Bulat Ibragimov, Gleb Gusev:
Minimal Variance Sampling in Stochastic Gradient Boosting. NeurIPS 2019: 15061-15071 - [i1]Bulat Ibragimov, Gleb Gusev:
Minimal Variance Sampling in Stochastic Gradient Boosting. CoRR abs/1910.13204 (2019) - 2018
- [j10]Hui Liu
Learning deconvolutional deep neural network for high resolution medical image reconstruction. Inf. Sci. 468: 142-154 (2018) - [c9]Yixuan Yuan
RIIS-DenseNet: Rotation-Invariant and Image Similarity Constrained Densely Connected Convolutional Network for Polyp Detection. MICCAI (2) 2018: 620-628 - [c8]Bulat Ibragimov, Diego A. S. Toesca, Yixuan Yuan
Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy. MICCAI (2) 2018: 684-692 - 2017
- [j9]Guoyan Zheng
Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge. Medical Image Anal. 35: 327-344 (2017) - [j8]Bulat Ibragimov
Segmentation of Pathological Structures by Landmark-Assisted Deformable Models. IEEE Trans. Medical Imaging 36(7): 1457-1469 (2017) - [c7]Robert Korez, Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
Intervertebral disc segmentation in MR images with 3D convolutional networks. Image Processing 2017: 1013306 - 2016
- [j7]Jianhua Yao
A multi-center milestone study of clinical vertebral CT segmentation. Comput. Medical Imaging Graph. 49: 16-28 (2016) - [j6]Ching-Wei Wang
A benchmark for comparison of dental radiography analysis algorithms. Medical Image Anal. 31: 63-76 (2016) - [c6]Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
Accurate landmark-based segmentation by incorporating landmark misdetections. ISBI 2016: 1072-1075 - 2015
- [j5]Bulat Ibragimov, Jerry L. Prince, Emi Z. Murano, Jonghye Woo, Maureen L. Stone, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
Segmentation of tongue muscles from super-resolution magnetic resonance images. Medical Image Anal. 20(1): 198-207 (2015) - [j4]Robert Korez, Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation. IEEE Trans. Medical Imaging 34(8): 1649-1662 (2015) - [j3]Ching-Wei Wang
Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE Trans. Medical Imaging 34(9): 1890-1900 (2015) - [c5]Robert Korez, Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
Deformable Model-Based Segmentation of Intervertebral Discs from MR Spine Images by Using the SSC Descriptor. CSI@MICCAI 2015: 117-124 - [c4]Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
Optimal reinforcement of training datasets in semi-supervised landmark-based segmentation. Image Processing 2015: 94132K - 2014
- [j2]Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
Shape Representation for Efficient Landmark-Based Segmentation in 3-D. IEEE Trans. Medical Imaging 33(4): 861-874 (2014) - 2013
- [c3]Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
Statistical shape representation with landmark clustering by solving the assignment problem. Image Processing 2013: 86690E - 2012
- [j1]Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
A Game-Theoretic Framework for Landmark-Based Image Segmentation. IEEE Trans. Medical Imaging 31(9): 1761-1776 (2012) - [c2]Bulat Ibragimov, Bostjan Likar, Franjo Pernus, Tomaz Vrtovec
Automated measurement of anterior and posterior acetabular sector angles. Computer-Aided Diagnosis 2012: 83151U - 2011
- [c1]Bulat Ibragimov, Bostjan Likar, Franjo Pernus:
Boundary detection by linear programming with application to lung fields segmentation. Image Processing 2011: 796231
Coauthor Index
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last updated on 2024-12-13 19:14 CET by the dblp team
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